Schema — Structure of Data. A schema is the description of the structure of your data (which together create a Dataset in Spark SQL). A schema is described using StructType which is a collection of StructField objects (that in turn are tuples of names, types, and nullability classifier).
StructType is a built-in data type that is a collection of StructFields. StructType is used to define a schema or its part. You can compare two StructType instances to see whether they are equal.
What is true of the Spark Interactive Shell? It initializes SparkContext and makes it available. Provides instant feedback as code is entered, and allows you to write programs interactively.
One use of Spark SQL is to execute SQL queries. Spark SQL can also be used to read data from an existing Hive installation. For more on how to configure this feature, please refer to the Hive Tables section. When running SQL from within another programming language the results will be returned as a Dataset/DataFrame.
How do I add a new column to a Spark DataFrame (using PySpark)?
- type(randomed_hours) # => list.
- # Create in Python and transform to RDD.
- new_col = pd.DataFrame(randomed_hours, columns=['new_col'])
- spark_new_col = sqlContext.createDataFrame(new_col)
- my_df_spark.withColumn("hours", spark_new_col["new_col"])
To create a new column, pass your desired column name to the first argument of withColumn() transformation function. Make sure this new column not already present on DataFrame, if it presents it updates the value of the column. On below snippet, lit() function is used to add a constant value to a DataFrame column.
- Creating an empty DataFrame (Spark 2. x and above)
- Create empty DataFrame with schema (StructType) Use createDataFrame() from SparkSession.
- Using implicit encoder. Let's see another way, which uses implicit encoders.
- Using case class. We can also create empty DataFrame with the schema we wanted from the scala case class.
Spark SQL – Module for Structured Data Processing. The computation layer is the place where we use the distributed processing of the Spark engine. The computation layer usually acts on the RDDs.
Spark session is a unified entry point of a spark application from Spark 2.0. It provides a way to interact with various spark's functionality with a lesser number of constructs. Instead of having a spark context, hive context, SQL context, now all of it is encapsulated in a Spark session.
Originally Answered: What is the difference between spark context and sparksession? sparkContext was used as a channel to access all spark functionality. SparkSession provides a single point of entry to interact with underlying Spark functionality and allows programming Spark with Dataframe and Dataset APIs.
SQLContext is a class and is used for initializing the functionalities of Spark SQL. SparkContext class object (sc) is required for initializing SQLContext class object. By default, the SparkContext object is initialized with the name sc when the spark-shell starts. Use the following command to create SQLContext.
Run Spark from the Spark Shell
- Navigate to the Spark-on-YARN installation directory, and insert your Spark version into the command. cd /opt/mapr/spark/spark-<version>/
- Issue the following command to run Spark from the Spark shell: On Spark 2.0.1 and later: ./bin/spark-shell --master yarn --deploy-mode client.
SparkContext – Introduction
It is the Main entry point to Spark Functionality. Generating, SparkContext is a most important task for Spark Driver Application and set up internal services and also constructs a connection to Spark execution environment.Spark applications can use multiple sessions to use different underlying data catalogs. You can use an existing Spark session to create a new session by calling the newSession method.
If you have an existing spark session and want to create new one, use the newSession method on the existing SparkSession. The newSession method creates a new spark session with isolated SQL configurations, temporary tables. The new session will share the underlying SparkContext and cached data.
implicits Object — Implicits Conversions. implicits object gives implicit conversions for converting Scala objects (incl. RDDs) into a Dataset , DataFrame , Columns or supporting such conversions (through Encoders). In Scala REPL-based environments, e.g. spark-shell , use :imports to know what imports are in scope.
class pyspark.sql.SparkSession(sparkContext, jsparkSession=None) The entry point to programming Spark with the Dataset and DataFrame API. A SparkSession can be used create DataFrame, register DataFrame as tables, execute SQL over tables, cache tables, and read parquet files.
The DataFrame API is available in Scala, Java, Python, and R. In Scala and Java, a DataFrame is represented by a Dataset of Row s. In the Scala API, DataFrame is simply a type alias of Dataset[Row] . While, in Java API, users need to use Dataset<Row> to represent a DataFrame .
Structs. A struct is similar to a case class: it stores a set of key-value pairs, with a fixed set of keys. If we convert an RDD of a case class containing nested case classes to a DataFrame, Spark will convert the nested objects to a struct.
RDD- When you want low-level transformation and actions, we use RDDs. Also, when we need high-level abstractions we use RDDs. DataFrame- We use dataframe when we need a high level of abstraction and for unstructured data, such as media streams or streams of text.
A Spark DataFrame is a distributed collection of data organized into named columns that provides operations to filter, group, or compute aggregates, and can be used with Spark SQL. DataFrames can be constructed from structured data files, existing RDDs, tables in Hive, or external databases.
DataFrame- In dataframe data is organized into named columns. Basically, it is as same as a table in a relational database. whereas, DataSets- As we know, it is an extension of dataframe API, which provides the functionality of type-safe, object-oriented programming interface of the RDD API.
A data frame is a table or a two-dimensional array-like structure in which each column contains values of one variable and each row contains one set of values from each column. The data stored in a data frame can be of numeric, factor or character type.
I am following these steps for creating a DataFrame from list of tuples:
- Create a list of tuples. Each tuple contains name of a person with age.
- Create a RDD from the list above.
- Convert each tuple to a row.
- Create a DataFrame by applying createDataFrame on RDD with the help of sqlContext.
spark dataset api with examples – tutorial 20. A Dataset is a strongly typed collection of domain-specific objects that can be transformed in parallel using functional or relational operations. Each Dataset also has an untyped view called a DataFrame, which is a Dataset of Row.
