Structuring Spark: DataFrames, Datasets, and Streaming

Structuring Spark
SQL, DataFrames, Datasets, and Streaming
Michael Armbrust- @michaelarmbrust
Spark Summit East 2016

Background: What is in an RDD?
•Dependencies
•Partitions (with optional locality info)
•Compute function: Partition => Iterator[T]
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•Dependencies
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Opaque Computation

•Dependencies
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Opaque Data

Struc·ture
[ˈstrək(t)SHər]
verb
1. construct or arrange according to a
plan; give a pattern or organization to.
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Why structure?
• By definition, structure will limit what can be
expressed.
• In practice, we can accommodate the vast
majority of computations.
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Limiting the space of what can be
expressed enables optimizations.

Structured APIs In Spark
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SQL DataFrames Datasets
Syntax
Errors
Analysis
Errors
Runtime Compile
Time
Runtime
Compile
Time
Compile
Time
Runtime
Analysis errors reported before a distributed job starts

Type-safe: operate
on domain objects
with compiled
lambda functions
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Datasets API
val df = ctx.read.json("people.json")
// Convert data to domain objects.
case class Person(name: String, age: Int)
val ds: Dataset[Person] = df.as[Person]
ds.filter(_.age > 30)
// Compute histogram of age by name.
val hist = ds.groupBy(_.name).mapGroups {
case (name, people: Iter[Person]) =>
val buckets = new Array[Int](10)
people.map(_.age).foreach { a =>
buckets(a / 10) += 1
}
(name, buckets)
}

DataFrame = Dataset[Row]
•Spark 2.0 will unify these APIs
•Stringly-typed methods will downcast to
generic Row objects
•Ask Spark SQL to enforce types on
generic rows using df.as[MyClass]
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What about ?
Some of the goals of the Dataset API have always been
available!
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df.map(lambda x: x.name)
df.map(x => x(0).asInstanceOf[String])

Shared Optimization & Execution
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SQL AST
DataFrame
Unresolved
Logical Plan
Logical Plan
Optimized
Logical Plan
RDDs
Selected
Physical Plan
Analysis
Logical
Optimization
Physical
Planning
CostModel
Physical
Plans
Code
Generation
Catalog
DataFrames, Datasets and SQL
sharethe same optimization/execution pipeline
Dataset

Columns
col("x") === 1
df("x") === 1
expr("x = 1")
sql("SELECT … WHERE x = 1")
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New value, computed based on input values.
DSL
SQL Parser

• 100+ native functionswith
optimized codegen
implementations
– String manipulation – concat,
format_string, lower, lpad
– Data/Time – current_timestamp,
date_format, date_add, …
– Math – sqrt, randn, …
– Other –
monotonicallyIncreasingId,
sparkPartitionId, …
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Complex Columns With Functions
from pyspark.sql.functions import *
yesterday = date_sub(current_date(), 1)
df2 = df.filter(df.created_at > yesterday)
import org.apache.spark.sql.functions._
val yesterday = date_sub(current_date(), 1)
val df2 = df.filter(df("created_at") > yesterday)

Functions
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(x: Int) => x == 1
Columns
col("x") === 1You Type
Spark Sees class $anonfun$1 {
def apply(Int): Boolean
}
EqualTo(x, Lit(1))

Columns: Predicate pushdown
sqlContext.read
.format("jdbc")
.option("url", "jdbc:postgresql:dbserver")
.option("dbtable", "people")
.load()
.where($"name" === "michael")
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You Write
Spark Translates
For Postgres SELECT * FROM people WHERE name = 'michael'

Columns: Efficient Joins
df1.join(df2, col("x") == col("y"))
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df1 df2
SortMergeJoin
myUDF = udf(lambda x, y: x == y)
df1.join(df2, myUDF(col("x"), col("y")))
df1 df2
Cartisian
Filter
n2
n log n
Equal values sort to
the same place

Spark's Structured Data Model
• Primitives: Byte, Short, Integer,Long, Float,
Double, Decimal,String, Binary, Boolean,
Timestamp, Date
• Array[Type]: variable length collection
• Struct: fixed # of nested columns with fixed types
• Map[Type, Type]: variable length association
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6 “bricks”
Tungsten’s Compact Encoding
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0x0 123 32L 48L 4 “data”
(123, “data”, “bricks”)
Null bitmap
Offset to data
Offset to data Field lengths

Encoders
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6 “bricks”0x0 123 32L 48L 4 “data”
JVM Object
Internal Representation
MyClass(123, “data”, “bricks”)
Encoders translate between domain
objects and Spark's internal format

Bridge Objects with Data Sources
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{
"name": "Michael",
"zip": "94709"
"languages": ["scala"]
}
case class Person(
name: String,
languages: Seq[String],
zip: Int)
Encoders map columns
to fields by name
{ JSON } JDBC

Operate Directly On Serialized Data
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df.where(df("year") > 2015)
GreaterThan(year#234, Literal(2015))
bool filter(Object baseObject) {
int offset = baseOffset + bitSetWidthInBytes + 3*8L;
int value = Platform.getInt(baseObject, offset);
return value34 > 2015;
}
DataFrame Code / SQL
Catalyst Expressions
Low-level bytecode
JVM intrinsic JIT-ed to
pointer arithmetic
Platform.getInt(baseObject, offset);

The simplest way to perform streaming analytics
is not having to reason about streaming.

Spark 2.0
Continuous DataFrames
Spark 1.3
Static DataFrames
Single API !

Structured Streaming
• High-level streaming API built on SparkSQL engine
• Runsthe same querieson DataFrames
• Eventtime, windowing,sessions,sources& sinks
• Unifies streaming, interactive and batch queries
• Aggregate data in a stream, then serve using JDBC
• Change queriesatruntime
• Build and apply ML models

logs = ctx.read.format("json").open("s3://logs")
logs.groupBy(logs.user_id).agg(sum(logs.time))
.write.format("jdbc")
.save("jdbc:mysql//...")
Example: Batch Aggregation

logs = ctx.read.format("json").stream("s3://logs")
logs.groupBy(logs.user_id).agg(sum(logs.time))
.write.format("jdbc")
.stream("jdbc:mysql//...")
Example: Continuous Aggregation

Logically:
DataFrame operations on static data
(i.e. as easyto understand as batch)
Physically:
Spark automatically runs the queryin
streaming fashion
(i.e. incrementally and continuously)
DataFrame
Logical Plan
Continuous,
incremental execution
Catalyst optimizer
Execution

Incrementalized By Spark
Scan Files
Aggregate
Write to MySQL
Scan New Files
Stateful
Aggregate
Update MySQL
Batch Continuous
Transformation
requires
information
about the
structure

What's Coming?
• Spark 2.0
• Unification ofthe APIs
• Basic streaming API
• Event-time aggregations
• Spark 2.1+
• Other streaming sources / sinks
• Machine learning
• Structurein other libraries: MLlib, GraphFrames
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Structuring Spark: DataFrames, Datasets, and Streaming

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