Spark DataFrames: Simple and Fast Analytics on Structured Data at Spark Summit 2015

Spark DataFrames:
Simple and Fast Analytics
on Structured Data
Michael Armbrust
Spark Summit 2015 - June, 15th

Graduated
from Alpha
in 1.3
• Spark SQL
•  Part of the core distribution since Spark 1.0 (April 2014)
SQL!About Me and
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0
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250
# Of Commits Per Month
0
50
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# of Contributors
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SELECT&COUNT(*)&
FROM&hiveTable&
WHERE&hive_udf(data)&&
• Spark SQL
•  Runs SQL / HiveQL queries, optionally alongside or
replacing existing Hive deployments
SQL!About Me and
Improved
multi-version
support in 1.4

4
• Spark SQL
•  Connect existing BI tools to Spark through JDBC
SQL!About Me and

• Spark SQL
•  Bindings in Python, Scala, Java, and R
5
SQL!About Me and

• Spark SQL
•  Bindings in Python, Scala, Java, and R
• @michaelarmbrust
•  Lead developer of Spark SQL @databricks
6
SQL!About Me and

The not-so-secret truth...
7
is about more than SQL.
!
SQL!

Spark SQL: The whole story
Creating and Running Spark Programs Faster:
•  Write less code
•  Read less data
•  Let the optimizer do the hard work
8

DataFrame
noun – [dey-tuh-freym]
9
1.  A distributed collection of rows organized into
named columns.
2.  An abstraction for selecting, filtering, aggregating
and plotting structured data (cf. R, Pandas).
3.  Archaic: Previously SchemaRDD (cf. Spark < 1.3).
!

Write Less Code: Input & Output
Unified interface to reading/writing data in a variety of formats:
df&=&sqlContext.read&&
&&.format("json")&&
&&.option("samplingRatio",&"0.1")&&
&&.load("/home/michael/data.json")&
&
df.write&&
&&.format("parquet")&&
&&.mode("append")&&
&&.partitionBy("year")&&
&&.saveAsTable("fasterData")!
! 10

&&.format("json")&&
&
df.write&&
&&.mode("append")&&
!
read and write&
functions create
new builders for
doing I/O
11

Builder methods
specify:
•  Format
•  Partitioning
•  Handling of
existing data
&&.format("json")&&
&
df.write&&
&&.mode("append")&&
! 12

load(…), save(…) or
saveAsTable(…)&
finish the I/O
specification
&&.format("json")&&
&
df.write&&
&&.mode("append")&&
! 13

Spark SQL’s Data Source API can read and write DataFrames
using a variety of formats.
14
{ JSON }
Built-In External
JDBC
and more…
Find more sources at http://spark-packages.org/

ETL Using Custom Data Sources
sqlContext.read&
&&.format("com.databricks.spark.jira")&
&&.option("url",&"https://issues.apache.org/jira/rest/api/latest/search")&
&&.option("user",&"marmbrus")&
&&.option("password",&"*******")&
&&.option("query",&"""&
&&&&|project&=&SPARK&AND&&
&&&&|component&=&SQL&AND&&
&&&&|(status&=&Open&OR&status&=&"In&Progress"&OR&status&=&Reopened)""".stripMargin)&
&&.load()&
&&.repartition(1)&
&&.write&
&&.format("parquet")&
&&.saveAsTable("sparkSqlJira")&
15

Write Less Code: High-Level Operations
Solve common problems concisely using DataFrame functions:
•  Selecting columns and filtering
•  Joining different data sources
•  Aggregation (count, sum, average, etc)
•  Plotting results with Pandas
16

Write Less Code: Compute an Average
private&IntWritable&one&=&&
&&new&IntWritable(1)&
private&IntWritable&output&=&
&&new&IntWritable()&
proctected&void&map(&
&&&&LongWritable&key,&
&&&&Text&value,&
&&&&Context&context)&{&
&&String[]&fields&=&value.split("t")&
&&output.set(Integer.parseInt(fields[1]))&
&&context.write(one,&output)&
}&
&
IntWritable&one&=&new&IntWritable(1)&
DoubleWritable&average&=&new&DoubleWritable()&
&
protected&void&reduce(&
&&&&IntWritable&key,&
&&&&Iterable<IntWritable>&values,&
&&&&Context&context)&{&
&&int&sum&=&0&
&&int&count&=&0&
&&for(IntWritable&value&:&values)&{&
&&&&&sum&+=&value.get()&
&&&&&count++&
&&&&}&
&&average.set(sum&/&(double)&count)&
&&context.Write(key,&average)&
}&
data&=&sc.textFile(...).split("t")&
data.map(lambda&x:&(x[0],&[x.[1],&1]))&&
&&&.reduceByKey(lambda&x,&y:&[x[0]&+&y[0],&x[1]&+&y[1]])&&
&&&.map(lambda&x:&[x[0],&x[1][0]&/&x[1][1]])&&
&&&.collect()&
17

Write Less Code: Compute an Average
Using RDDs
&
data&=&sc.textFile(...).split("t")&
data.map(lambda&x:&(x[0],&[int(x[1]),&1]))&&
&&&.reduceByKey(lambda&x,&y:&[x[0]&+&y[0],&x[1]&+&y[1]])&&
&&&.map(lambda&x:&[x[0],&x[1][0]&/&x[1][1]])&&
&&&.collect()&
&
&
&Using DataFrames
&
sqlCtx.table("people")&&
&&&.groupBy("name")&&
&&&.agg("name",&avg("age"))&&
&&&.collect()&&
!
Full API Docs
•  Python
•  Scala
•  Java
•  R
18

Not Just Less Code: Faster Implementations
19
0 2 4 6 8 10
RDD Scala
RDD Python
DataFrame Scala
DataFrame Python
DataFrame R
DataFrame SQL
Time to Aggregate 10 million int pairs (secs)

20
Demo
Combine data from with data from
Running in
•  Hosted Spark in the cloud
•  Notebooks with integrated visualization
•  Scheduled production jobs
https://accounts.cloud.databricks.com/

6/15/2015 demo - Databricks
https://demo.cloud.databricks.com/#notebook/43587 1/5
>
Command took 2.08s -- by dbadmin at 6/15/2015, 1:17:07 PM on michael (54 GB)
>
>
%run /home/michael/ss.2015.demo/spark.sql.lib ...
%sql SELECT * FROM sparkSqlJira

>
rawPRs: org.apache.spark.sql.DataFrame = [commenters: array<struct<data:struct<asked_to_close:boolea
n,avatar:string,body:string,date:array<string>,diff_hunk:string,said_lgtm:boolean,url:string>,usernam
e:string>>, components: array<string>, is_mergeable: boolean, jira_issuetype_icon_url: string, jira_i
ssuetype_name: string, jira_priority_icon_url: string, jira_priority_name: string, last_jenkins_comme
nt: struct<body:string,html_url:string,user:struct<login:string>>, last_jenkins_outcome: string, line
s_added: bigint, lines_changed: bigint, lines_deleted: bigint, number: bigint, parsed_title: struct<j
iras:array<bigint>,metadata:string,title:string>, state: string, updated_at: string, user: string]
>
val rawPRs = sqlContext.read
.format("com.databricks.spark.rest")
.option("url", "https://spark-prs.appspot.com/search-open-prs")
.load()
display(rawPRs)

>
import org.apache.spark.sql.functions._
sparkPRs: org.apache.spark.sql.DataFrame = [component: string, pr_jira: string, title: string, jira_i
ssuetype_icon_url: string, jira_priority_icon_url: string, number: bigint, commenters: array<struct<d
ata:struct<asked_to_close:boolean,avatar:string,body:string,date:array<string>,diff_hunk:string,sai
d_lgtm:boolean,url:string>,username:string>>, user: string, last_jenkins_outcome: string, is_mergeabl
e: boolean]
import org.apache.spark.sql.functions._
val sparkPRs = rawPRs
.select(
// "Explode" nested array to create one row per item.
explode($"components").as("component"),

// Use a built-in function to construct the full 'SPARK-XXXX' key
concat("SPARK-", $"parsed_title.jiras"(0)).as("pr_jira"),
// Other required columns.
$"parsed_title.title",
$"jira_issuetype_icon_url",
$"jira_priority_icon_url",
$"number",
$"commenters",
$"user",
$"last_jenkins_outcome",
$"is_mergeable")
.where($"component" === "SQL") // Select only SQL PRs

>
✗
✗
✗
✗
table("sparkSqlJira")
.join(sparkPRs, $"key" === $"pr_jira")
.jiraTable

Plan Optimization & Execution
21
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 and SQL share the same optimization/execution pipeline

Seamlessly Integrated
Intermix DataFrame operations with
custom Python, Java, R, or Scala code
zipToCity&=&udf(lambda&zipCode:&<custom&logic&here>)&
&
def&add_demographics(events):&
&&&u&=&sqlCtx.table("users")&
&&&events&&
&&&&&.join(u,&events.user_id&==&u.user_id)&&&&&&&
&&&&&.withColumn("city",&zipToCity(df.zip))&
Augments any
DataFrame
that contains
user_id&
22

Optimize Entire Pipelines
Optimization happens as late as possible, therefore
Spark SQL can optimize even across functions.
23
events&=&add_demographics(sqlCtx.load("/data/events",&"json"))&
&&
training_data&=&events&&
&&.where(events.city&==&"San&Francisco")&&
&&.select(events.timestamp)&&
&&.collect()&&

24
&&&u&=&sqlCtx.table("users")&&&&&&&&&&&&&&&&&&&&&#&Load&Hive&table&
&&&events&&
&&&&&.join(u,&events.user_id&==&u.user_id)&&&&&&#&Join&on&user_id&&&&&&
&&&&&.withColumn("city",&zipToCity(df.zip))&&&&&&#&Run&udf&to&add&city&column&
&events&=&add_demographics(sqlCtx.load("/data/events",&"json"))&&
training_data&=&events.where(events.city&==&"San&Francisco").select(events.timestamp).collect()&&
Logical Plan
filter
join
events file users table
expensive
only join
relevent users
Physical Plan
join
scan
(events)
filter
scan
(users)
24

25
&&&u&=&sqlCtx.table("users")&&&&&&&&&&&&&&&&&&&&&#&Load&partitioned&Hive&table&
&&&events&&
&&&&&.join(u,&events.user_id&==&u.user_id)&&&&&&#&Join&on&user_id&&&&&&
&&&&&.withColumn("city",&zipToCity(df.zip))&&&&&&#&Run&udf&to&add&city&column&
&
Optimized Physical Plan
with Predicate Pushdown
and Column Pruning
join
optimized
scan
(events)
optimized
scan
(users)
events&=&add_demographics(sqlCtx.load("/data/events",&"parquet"))&&
training_data&=&events.where(events.city&==&"San&Francisco").select(events.timestamp).collect()&&
Logical Plan
filter
join
events file users table
Physical Plan
join
scan
(events)
filter
scan
(users)
25

Machine Learning Pipelines
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tokenizer&=&Tokenizer(inputCol="text",!outputCol="words”)&
hashingTF&=&HashingTF(inputCol="words",!outputCol="features”)&
lr&=&LogisticRegression(maxIter=10,&regParam=0.01)&
pipeline&=&Pipeline(stages=[tokenizer,&hashingTF,&lr])&
&
df&=&sqlCtx.load("/path/to/data")!
model&=&pipeline.fit(df)!
ds0 ds1 ds2 ds3tokenizer hashingTF lr.model
lr
Pipeline Model!
Find out more during Joseph’s Talk: 3pm Today

Project Tungsten: Initial Results
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0
50
100
150
200
1x 2x 4x 8x 16x
Average GC
time per
node
(seconds)
Data set size (relative)
Default
Code Gen
Tungsten onheap
Tungsten offheap
Find out more during Josh’s Talk: 5pm Tomorrow

Questions?
Spark SQL Office Hours Today
-  Michael Armbrust 1:45-2:30
-  Yin Huai 3:40-4:15
Spark SQL Office Hours Tomorrow
-  Reynold 1:45-2:30

Spark DataFrames: Simple and Fast Analytics on Structured Data at Spark Summit 2015

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