Spark SQL is the module that brings SQL support to Spark for processing structured data stored in its distributed datasets (Spark RDDs) and external data sources. Spark SQL provides an integrated development experience for developers by allowing them to merge SQL queries with functions from Dataframes API. Also, it enables accessing and joining data from various databases such as JDBC, Hive, Parquet, Avro, ORC, and JSON.
Its integration with Hive allows developers to query Hive warehouses. Also, developers can use existing Business Intelligence tools by connecting through standard JDBC and ODBC connectivity provided by the Spark SQL server mode.
Spark SQL Architecture
Spark SQL runs on top of Sparks’ core execution engine as a library and uses JDBC/ODBC, command-line console, and user programs to expose SQL interfaces.
Major components of the architecture include:
- Dataset and DataFrame APIs
Dataframes are like relational tables in SQL. They are ‘datasets’ or distributed data collections that can be constructed using various data sources and organized into named columns. Data Frame API allows you to manipulate DataFrames and combine them with SQL queries.
- Data Sources API
This API enables unified data access from data sources like Hive, Avro, Parquet, ORC, JSON, and JDBC. Especially Spark makes tasks easier to pull data from Apache Hive tables and Parquets’ columnar data. Also, it enables data manipulation in any language that Spark supports.
- Catalyst Optimizer
This Catalyst optimizer uses Scala’s’ functional programming constructs for adding optimizations for Spark SQL and enables extending it by external developers.
Why Use Spark SQL?
If you have SQL skills, Spark SQL allows you to manipulate data using SQL in the Spark environment. It is easy to learn and simplifies using Sparks’ internal data structures with SQL. Spark SQL is the right choice if you are building sophisticated applications that need data from various data sources in a distributed environment.
Spark SQL makes running interactive queries easy on large volumes of data in a distributed environment. It allows using Spark SQL inside spark programs written in Java, Scala, Python, and R, so you never have to worry about such programs’ database query compatibility.
Besides, if you are working on retrieving insights from larger data sets using Business Intelligence tools, Spark SQLs’ allows you to connect with them using industry-standard connectivity tools. Also, it is good for building applications that need high scalability because of its ability to tolerate mid-query faults and catalyst optimization.
Advantages and Disadvantages of Spark SQL
- Spark SQL makes queries fast using its cost-based optimizer, data frames, and code generation.
- Using spark engine, it can scale to thousands of nodes supporting mid-query fault tolerance.
- It allows access to data from different sources like Hive, Avro, Parquet, ORC, JSON, and JDBC, providing unified data access.
- Compatible with HiveQL syntax, Hive SerDes, and UDFs for Hive warehouse access.
- Can use it with Spark programs written in Java, Scala, Python, and R
- It has larger community support to get updated with each spark release.
- Unsupportive to tables with union fields
- Hive transactions are not supported
- Unsupportive of the Char data type
- Lack of error handling when the varchar type gets oversized.
Spark SQL provides SQL support for structured data processing in Spark. It has many intuitive features to simplify querying distributed data sets, faster data processing, and achieve fault tolerance and scalability. It runs on top of Sparks’ execution engine. Despite its many advantages, there are also a few disadvantages like lack of support for some data types.