7 shining Apache Spark SQL Features: A Quick Guide

1. Objective

There are several shining Spark SQL features available. In this article, we will focus on all those features of SparkSQL, such as unified data access, high compatibility and many more. Although, We will study each feature in detail. However, to understand features of Spark SQL well, we will first learn brief introduction to Spark SQL.

Spark SQL Features

Features of Spark SQL

2. Introduction to Spark SQL

While we talk about working with structured data, the name strikes are Spark SQL. Basically, it supports distributed in-memory computations on a huge scale. Basically, Spark SQL Proclaims the information about the structure of both computations as well as data. However, at the time of extra optimizations, this extra information turns very helpful. In addition, we can easily execute SQL queries through it.

Moreover, SparkSQL can be used to read data from an existing Hive installation. However, when SQL run in another programming language, the output comes as dataset/dataFrame. Moreover, we can use the command-line or over JDBC/ODBC to interact with the SQL interface.

Through Spark SQL, There are 3 main capabilities of using structured and semi-structured data, such as:

  1. Spark SQL do grant a dataframe abstraction in following languages, such as Scala, Java, as well as Python. Moreover, it simplifies working with structured datasets. Also, dataframes are similar to tables in a relational database.
  2. Basically, through SQL, we can write and read data in several structured formats, such as Hive Tables, JSON and Parquet.
  3. It is possible to query the data by using Spark SQL. Moreover, inside a Spark program as well as from external tools that connect to SQL of Spark.

The major advantage which Spark SQL leverages is that developers can switch back and forth between different APIs. Although it is similar to Spark. Therefore, it bestows the most natural way to express the given transformations.

3. Spark SQL Features

a. Unified Data Access

Basically, it supports a common way to access a variety of data sources, for example, Hive, Avro, Parquet, ORC, JSON, and JDBC. Moreover, we can also join the data from these sources. However, in order to accommodate all the existing users into Spark SQL, it is very helpful.

b. Scalability

SparkSQL leverages advantage of RDD model. Basically, it supports large jobs and mid-query fault tolerance. Moreover, for both interactive and long queries, it uses the same engine.

c. High compatibility

While it comes to unmodified Hive queries we are allowed to run them on existing warehouses in Spark SQL. Also, it provides full compatibility with existing Hive data, queries as well as UDFs. Further, rewrites the MetaStore as well as Hive frontend.

d. Integrated

The word integrate means as combining or merge. Basically,  we can integrate Spark SQL with Spark programs. Moreover,  Spark SQL allows us to query structured data inside Spark programs. However, it is possible by using SQL or a DataFrame that can be used in Java, Scala.

In addition, it is possible to run streaming computation through it. Also, to run it developers write a batch computation against the dataframe / dataset API. Moreover, to run it in a streaming fashion Spark itself increments the computation. The only advantage is that developers don’t have to manage state, failures on own. Also, there is no requirement to keep the application in sync with batch jobs. Apart from it, the streaming job always gives the same answer as a batch job on the same data.

e. Standard Connectivity

Basically, we can easily interface Spark SQL with JDBC or ODBC. However, for connectivity for business intelligence tools, both turned as industry norms. Hence, it consists industry standard JDBC and ODBC connectivity with server mode.

f. For batch processing of Hive Tables

In case of Hive tables, SparkSQL can be used for batch processing in them.

g. Performance Optimization

Basically, in SparkSQL, when it comes to querying optimization engine. It converts each SQL query into a logical plan. Moreover, it also converts to many physical execution plans. However, it chooses the most optimal physical plan, across the entire plan, at the time of execution. Also, it ensures fast execution of HIVE queries.

4. Spark SQL Features – Conclusion

As a result, we have learned all Apache Spark SQL features in detail. Hence, we have also seen, how Spark SQL work as a Spark module that analyses structured data. However, SparkSQL provides scalability. Also, ensures high compatibility with the system. Moreover, by using JDBC or ODBC it also allows standard connectivity. Thus, all these features enhance its working efficiency. Although, we have tried to cover each aspect regarding still if you want to ask any query, feel free to ask in the comment section.

Top books for learning Spark.

For reference


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