SparkSQL

The primary goal of Spark SQL Optimization is to improve the SQL query run-time performance reducing the query’s time and memory consumption, saving organizations time and money.

Analysis

Logical Optimization

Physical Planning

Code Generation

Let’s review the four Catalyst phases:

  • In the Analysis phase, Catalyst analyzes the query, the DataFrame, the unresolved logical plan, and the Catalog to create a logical plan.

  • During the Logical Optimization phase, the Logical plan evolves into an Optimized Logical Plan. This is the rule-based optimization step of Spark SQL and rules such as folding, pushdown, and pruning are applied here.

  • During the Physical Planning phase, Catalyst generates multiple physical plans based on the Logical Plan. A physical plan describes computation on datasets with specific definitions on how to conduct the computation. A cost model then chooses the physical plan with the least cost. This is the cost-based optimization step.

  • The final phase is Code Generation. Catalyst applies the selected physical plan and generates Java bytecode to run on each node.

Example

  • SparkSQL is a Spark module for structured data processing.

  • You can interact with Spark SQL using SQL queries and the DataFrame API.

  • Spark SQL supports Java, Scala, Python, and R APIs.

  • Spark SQL uses the same execution engine to compute the result independently of which API or language you are using for the computation.

results = spark.sql(
        "SELECT  * FROM  people")
names = results.map(lambda p:p.name)

Above is an example of a Spark SQL query using Python.

  • The query select ALL rows from people statement is the SQL query run using Spark SQL.
  • The entity “people” was registered as a table view before this command.
  • Unlike the basic Spark RDD API, Spark SQL includes a cost-based optimizer, columnar storage, and code generation to perform optimizations that provide Spark with additional information about the structure of both the data and the computation in process.

Tungsten

  • Tungsten optimizes the performance of underlying hardware focusing on CPU performance instead of IO. Java was initially designed for transactional applications.
  • Tungsten aims to improve CPU and Memory performance by using a method more suited to data processing for the JVM to process data.
  • To achieve optimal CPU performance, Tungsten applies the following capabilities:
    • Tungsten manages memory explicitly and does not rely on the JVM object model or garbage collection.
    • Tungsten creates cache-friendly data structures that are arranged easily and more securely using STRIDE-based memory access instead of random memory access.
    • Tungsten supports JVM bytecode on-demand.
    • Tungsten does not enable virtual function dispatches, reducing multiple CPU calls.
    • Tungsten places intermediate data in CPU registers and enables loop unrolling.

SparkSQL - How To

Recap:

  • Spark SQL Is a Spark module for structured data processing.
  • Spark SQL is used to run SQL queries on Spark DataFrames and has available APIs in Java, Scala, Python, and R.
  • The first step to running SQL queries in Spark SQL is to create a table view.
  • A table view is a temporary table used to run SQL queries.
  • Spark SQL supports both temporary and global temporary table views.
  • A temporary view has local scope. Local scope means that the view exists only within the current spark session on the current node.
  • A global temporary view, however, exists within the general Spark application. A global temporary view is shareable across different Spark sessions.

Create a DF from JSON

# Create a DF from json file
df = spark.read.json("people.json")

Create Temp View

df.createTempView("people")

SparkSQL Queries

spark.sql("SELECT *
           FROM   people").show()

Create Global View

df.createGlobalTempView("people")

# query the table
spark.sql("SELECT * 
           FROM  global_temp.people").show()
  • Note the minor syntax change, including the “Global” prefix to the function name and the “global temp” prefix to the view name.

Aggregate

  • Aggregation, a standard Spark SQL process, is generally used to present grouped statistics.
  • DataFrames come inbuilt with commonly used aggregation functions such as count, average, max, and others.
  • You can also perform aggregation programmatically using SQL queries and table views.

Import CSV to DF

import pandas as pd
mtcars = pd.read_csv('mtcars.csv')
sdf = spark.createDataFrame(mtcars)

Explore Data

sdf.select('mpg').show(5)

+----+
| mpg|
+----+
|21.0|
|21.0|
|22.8|
|21.4|
|18.7|
+----+
only showing top 5 rows

Aggregate with DF Functions

{python}
car_counts = sdf.groupby(['cyl'])\
                        .agg({"wt": "count"})\
                        .sort("count(wt)", ascending=False)\
                        .show(5)
                        
+---+---------+
|cyl|count(wt)|
+---+---------+
|  8|       14|
|  4|       11|
|  6|        7|
+---+---------+

Aggregate with SparkSQL

# Create local view
sdf.createTempView("cars")

# Query local table
sql(" SELECT cyl, COUNT(*) 
        FROM cars
        GROUPBY cyl
        ORDER by 2 Desc")
        
+---+---------+
|cyl|count(wt)|
+---+---------+
|  8|       14|
|  4|       11|
|  6|        7|
+---+---------+

Next, let’s look at some of the data sources that Spark SQL supports.

  • Parquet is a columnar format supported by many data processing systems. Spark SQL supports reading and writing data from Parquet files, and Spark SQL preserves the data schema.
  • Similarly, Spark SQL can load and write to JSON datasets by inferring the schema.
  • Spark SQL also supports reading and writing data stored in Hive.