Spark SQL Features You must know

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1. Objective

In this document, we will see various shining Spark SQL features. There are many features Like Unified Data Access, High Compatibility and many more. We will focus on each feature in detail. But, before learning features of Spark SQL, we will also study brief introduction to Spark SQL.

Spark SQL Features

Spark SQL Features

2. Introduction to Spark SQL

In Apache Spark, Spark SQL is a module for working with structured data. Spark SQL supports distributed in-memory computations on a huge scale. It divulges the information about the structure of both computations as well as data. To perform extra optimizations, this extra information turns very helpful. We can easily execute SQL queries through it.
In addition, to read data from an existing Hive installation, we can use Spark SQL. The results come as Dataset/DataFrame When SQL run in another programming language. We can interact with the SQL interface, by using the command-line or over JDBC/ODBC.
The 3 main capabilities of using structured and semi-structured data, by Spark SQL. Such as:

  • It grants a DataFrame abstraction in Scala, Java, as well as Python. Also,  simplifies working with structured datasets. Here, DataFrames are similar to tables in a relational database.
  • In various structured formats, Spark SQL can read and write data. For Example Hive Tables, JSON and Parquet.
  •  We can query the data by using Spark SQL. Both inside a Spark program and from external tools that connect to Spark SQL.

In Spark SQL, developers can switch back and forth between different APIs, as same as Spark. Therefore, it bestows the most natural way to express the given Transformations.

3. Spark SQL features

a. Integrated

Integrate is simply defined as combining or merge. Here, Spark SQL queries are integrated with Spark programs. Through Spark SQL we are allowed to query structured data inside Spark programs. This is possible by using SQL or a DataFrame that can be used in Java, Scala.
We can run streaming computation through it. Developers write a batch computation against the DataFrame / Dataset API to run it. After that to run it in a streaming fashion Spark itself increments the computation. Developers leverage the advantage of it that they don’t have to manage state, failures on own. Even no need keep the application in sync with batch jobs. Despite, the streaming job always gives the same answer as a batch job on the same data.

b. Unified Data Access

To access a variety of data sources DataFrames and SQL support a common way. Data Sources like Hive, Avro, Parquet, ORC, JSON, as well as JDBC. It helps to join the data from these sources. To accommodate all the existing users into Spark SQL, it turns out to be very helpful.

c. High compatibility

We are allowed to run unmodified Hive queries on existing warehouses in Spark SQL. With existing Hive data, queries and UDFs, Spark SQL offers full compatibility. Also, rewrites the MetaStore and Hive frontend.

d. Standard Connectivity

We can easily connect Spark SQL through JDBC or ODBC.  For connectivity for business intelligence tools, Both turned as industry norms. Also, includes industry standard JDBC and ODBC connectivity with server mode.

e. Scalability

It takes advantage of RDD model, to support large jobs and mid-query fault tolerance. For interactive as well as long queries, it uses the same engine.

f. Performance Optimization

In Spark SQL, query optimization engine converts each SQL query into a logical plan. Afterwards, it converts to many physical execution plans.  At the time of execution, it selects the most optimal physical plan, among the entire plan. It ensures fast execution of HIVE queries.

g. For  batch processing of Hive tables

While working with Hive tables, we can use Spark SQL for Batch Processing in them.

4. Conclusion

Hence, we have seen all Spark SQL features in detail. As a result, we have learned,  Spark SQL is a module of Spark that analyses structured data. Basically, it offers scalability and ensures high compatibility of the system. Also, allow standard connectivity through JDBC or ODBC. Therefore, it bestows the most natural way to express the structured data. Moreover, it enhances its working efficiency with above-mentioned Spark SQL features.

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