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ORC , PARQUET and Avro Highlighted

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ORC AND PARQUET AND AVRO


File formats in Hadoop: Parquet, Avro and ORC


In this post, we will talk about the three file formats that exist for the Hadoop ecosystem, and we will review some common features of file formats and compare them with each other.

Hadoop, like any standard file system, allows you to store information in any format, whether structured, semi-structured or unstructured.

It also supports optimized formats for storage and processing in HDFS.

You have many options for storing and processing data in Hadoop that you can use depending on your needs.

In fact, Hadoop does not have a default file format, and choosing the right file format depends on your usage.

The data may be readable to us in formats such as JSON, XML or a CSV file, but that does not mean that this is the best way to actually store data in Hadoop.

In fact, storing data in text in JSON or CSV format is very inefficient because these file formats can not be saved in parallel and also takes time to search for a specific field because there is no index on the data and must be single Search single records for specific data.

In addition, such files occupy a large volume; So if the file can be compressed, a large amount of disk space will be saved.

In this regard, optimal file formats for working with big data have been developed, which are:

  • Avro format

  • Parquet format

  • Optimized Row Columnar Format (ORC)


Avro file format:


Avro file format


Resource of IMG :

http://parquet.apache.org/

https://avro.apache.org/


  • A row storage format that is able to compress data efficiently.   

  • Avro format is a binary data storage protocol that provides data serialization and data transfer services.   

  • Saves data in JSON format for easy data reading and interpretation.   

  • A key feature of the Avro format is its strong support for data schema, which changes over time.

  • Avro controls data schema changes such as missing fields, added fields, and changed fields.   

  • This file format has a high degree of flexibility, but compared to columnar file formats, it has less compression.   

  • It is not optimal for fast file search due to its row storage format.   

  • However, if you want to save your data as JSON in Hadoop so that you can easily change the data later, Avro is for it.


Parquet file format:

Parquet file format

Resource of IMG :

http://parquet.apache.org/

https://avro.apache.org/


  • This file format was created by Cloudra and Twitter in 2013.   

  • A unique feature of Parquet is that it can store data with nested structures in columns.

  • However, nested fields can be read separately without reading all the nested structure fields.   

  • This format is suitable for working with large volumes of complex data and offers various data compression and encoding options.   

  • This file format is very useful for reading specific columns from large tables, as it can only read the required columns instead of the entire table. This leads to faster data processing and reduces I / O referral time.   

  • Column storage capability allows you to quickly filter unrelated data during queries.   

  • There are different codecs for data compression and data files can have different types of compression.


ORC file format:

PAR file format

Resource of IMG :

http://parquet.apache.org/

https://avro.apache.org/


  • This format is optimized for reading, writing and processing data in Hive and was created by Hortonworks in 2013 to speed up Hive.   

  • The ORC file format stores a set of rows in a file so that each row of data has a column format.   

  • Stores data compactly and allows you to skip unrelated parts without the need for complex or manual indexing.

  • Supports decimal data types, dates and complex types (struct, list, map and union).Ability to read a file simultaneously using separate RecordReaders.

  • Generally in column file format, data read speed is high; Therefore, they are suitable for analytical work, while in the format of row files, the speed of writing data is high; Therefore, they are suitable for heavy transaction writing.


Does ORC support schema evolution?

ORC or any other format supports plot evolution (adding new columns) by adding columns at the end of the plot.

ORC as a schema in reading: Like Avro, ORC supports schema in reading and ORC data files contain data plans along with data statistics.


Does parquet support schema evolution?

Schema integrations such as Protocol Buffer, Avro and Thrift, Parquet also support the evolution of the scheme. Users can start with a simple layout and gradually add more columns as needed. In this way, users may encounter multiple parquet files with different but compatible schemas.

How does a schema evolve?

What is schema evolution? Schema evolution is a feature that allows users to easily modify the current table schema to accommodate data that is changing over time. It is usually used to include one or more new columns when performing concatenation or rewriting operations to automatically match the schema.
How do you deal with schema evolution in Hive?

How to manage schema changes / evolutions in Hive ORC tables such as column deletions that occur in the DB source.

    Before the schema changes: ...


schema

 

Which is better, ORC or parquet?


  • PARQUET has more ability to store nested data.

  • ORC has more capabilities for Pushdown Predicate.
    ORC supports ACID features.

  • ORC compression is more efficient.


Why is parquet better than ORC?

  • ORC indexes are used only to select bars and row groups, not to answer queries.

  • AVRO is a row-based storage format while PARQUET is a column-based storage format.

  • PARQUET is much better for analytical queries, meaning reading and queries are much more efficient than writing.


Why is ORC faster?

We all know that parquet and ORC are both columnar file storage. Use any compression algorithm to compress large data and store with much less space. ...

Parquet, ORC integrates well with the entire Hadoop ecosystem and extracts results much faster than traditional filesystem files such as json, csv, txt files.


What is the best file format for schema evolution in Hive?
Using ORC files improves performance when reading, writing and processing data compared to Text, Sequence and Rc.

RC and ORC perform better than Text and Sequence File formats.


Is Hive SQL case sensitive?
No. The hive is not case sensitive.


What is the evolution of schema in Avro?

Schema evolution allows you to update the schema used to write new data, while maintaining compatibility with your old data design (s).

Then you can read them all together as if all the data had a schema. Of course, to maintain consistency, strict rules govern permitted changes.



Does Avro support schema evolution?

Fortunately, Thrift, Protobuf, and Avro all support the evolution of the design: you can modify the design, you can have manufacturers and consumers with different versions of the design at the same time, and they all continue to work.



How does Avro manage schema evolution?
One of the key features of Avro is the strong support for data schemas that change over time - design evolution. Avro controls schema changes such as missing fields, added fields, and changed fields.

As a result, older applications can read new data and newer applications can read older data.


How do I know if my design is compatible?
To confirm the compatibility of a certain design, you can test it in one of two ways:

Using the Schema Registry Maven plugin ....

Using compatibility types

  •     In your client application

  •     Using the Schema Registry REST API.

  •     Using the Control Center Edit Schema feature. See Schema Management for Themes.


Is Auro faster than parquet?
Avro is fast in recovery, parquet is much faster. Parquet stores data as a combination on disk.

It performs a horizontal partition of the data and stores each partition as a column.


Does the parquet file have a schema?
Parquet file is an hdf file that must contain the file metadata.

This allows you to split columns into multiple files, and also allows a metadata file to refer to just a few parquet files. The metadata contains a layout for the data stored in the file.


Does parquet have a pattern?

Parquet uses compact and column data display in HDFS.

In a parquet file, metadata (defining a parquet schema) contains data structure information that is written after the data to allow a pass to be written.


Is Pyspark case sensitive?

Although Spark SQL itself is not case sensitive, Hive compatible file formats such as Parquet are case sensitive. Spark SQL should use an uppercase and lowercase design, when queries from any table supported by files containing uppercase field letters may not provide accurate results.


What kind of restrictions can the Hive key have?
Hive now allows users to announce the following restrictions: PRIMARY KEY. External key. Exclusive.

Is the SQL spark column case sensitive?
when the spark. sql. caseSensitive is set to false, Spark separates the insensitive column name between the Hive metastore and Parquet schema, so even if the column names are in different letters, Spark returns the corresponding column values.

Are CSV files divisible?
* CSV can be split when it is a raw and uncompressed file or uses a split compression format such as BZIP2 or LZO (Note: LZO must be indexed to be split!) ... for uses that require Working on complete rows of data, a format such as CSV, JSON or even AVRO must be used.


What are Avro and ORC?
The biggest difference between ORC, Avro and Parket is how the data is stored.

Parquet and ORC both store data in columns, while Avro stores data in rows. ...

While column-based stores such as Parquet and ORC are superior in some cases, in others row-based storage mechanisms such as Avro may be a better choice.


Is the ORC file compressed?
The ORC file format offers the following benefits: Efficient compression: Stored in columns and compressed, resulting in smaller disk readings. The column template is also ideal for vector optimization in Tez.

Why is ORC good for Hive?
Optimal Row (ORC) column file format is a very efficient way to store Hive data.

It is designed to overcome the limitations of other Hive file formats. In case Hive is reading or writing or processing data using ORC files improves performance


Is ORC a column?

ORC is a column storage format used in Hadoop for Hive tables. It is an efficient file format for storing data in which records contain many columns.


Does Spark support ORC?

ORC Spark support takes advantage of recent improvements to the data source API available in Spark 1.4 (SPARK-5180). ...

Because ORC is one of the main file formats supported in Apache Hive, users of the SQL and DataFrame Spark APIs will now have instant access to the ORC data in Hive tables.


Thank you.

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