It provides a general framework for transforming trees, which is used to perform analysis/evaluation, optimization, planning, and run time code spawning.It is the newest and most technically evolved component of SparkSQL.SQL Interpreter and Optimizer is based on functional programming constructed in Scala. It is equivalent to a relational table in SQL used for storing data into tables. Provides API for Python, Java, Scala, and R Programming.Ī DataFrame is a distributed collection of data organized into named columns.Can be easily integrated with all Big Data tools and frameworks via Spark-Core.Supports different data formats (Avro, CSV, Elastic Search, and Cassandra) and storage systems ( HDFS, HIVE Tables, MySQL, etc.).It processes the data in the size of Kilobytes to Petabytes on a single-node cluster to multi-node clusters.It is lazily evaluated like Apache Spark Transformations and can be accessed through SQL Context and Hive Context.DataFrame API is a distributed collection of data in the form of named column and row.It is a Data Abstraction and Domain Specific Language (DSL) applicable to structure and semi-structured data.Supports third-party integration through Spark packages.It has built-in support for Hive, Avro, JSON, JDBC, Parquet, etc.This is a universal API for loading and storing structured data. Data Source API (Application Programming Interface): Spark SQL has the following four libraries which are used to interact with relational and procedural processing: 1. It is easy to run locally on one machine - all you need is to have java installed on your system PATH, or the JAVA_HOME environment variable pointing to a Java installation.įigure: Architecture of Spark SQL. Spark runs on both Windows and UNIX-like systems (e.g. It introduces an extensible optimizer called Catalyst as it helps in supporting a wide range of data sources and algorithms in Big-data. Spark SQL provides DataFrame APIs which perform relational operations on both external data sources and Spark’s built-in distributed collections. With Spark SQL, Apache Spark is accessible to more users and improves optimization for the current ones. DataFrame API and Datasets API are the ways to interact with Spark SQL. It offers much tighter integration between relational and procedural processing, through declarative DataFrame APIs which integrates with Spark code. Spark SQL blurs the line between RDD and relational table. Let us explore, what Spark SQL has to offer. It majorly works on DataFrames which are the programming abstraction and usually act as a distributed SQL query engine. Spark SQL is not a database but a module that is used for structured data processing. But the question which still pertains in most of our minds is, Is Spark SQL a database? These drawbacks gave way to the birth of Spark SQL. To overcome this, users have to use the Purge option to skip trash instead of drop.
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