Making Nested Columns as First Citizen in Apache Spark SQL

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Spark+AI SF
•
Making Nested Columns as First
Citizens in Apache Spark SQL
Cesar Delgado @hpcfarmer
DB Tsai @dbtsai

Siri
The world’s most popular intelligent assistant service powering
every iPhone, iPad, Mac, Apple TV, Apple Watch, and HomePod

Siri Open Source Team
• We’re Spark, Hadoop, HBase PMCs / Committers / Contributors
• We’re the advocate for Open Source
• Pushing our internal changes back to the upstreams
• Working with the communities to review pull requests, develop
new features and bug fixes

Siri Data
• Machine learning is used to personalize your experience
throughout your day
• We believe privacy is a fundamental human right

Siri Scale
• Large amounts of requests, Data Centers all over the world
• Hadoop / Yarn Cluster has thousands of nodes
• HDFS has hundred of PB
• 100's TB of raw event data per day
• More than 90% of jobs are Spark
• Less than 10% are legacy Pig and MapReduce jobs

Details about our data
• Deeply nested relational model data with couple top level columns
• The total nested fields are more than 2k
• Stored in Parquet format partitioned by UTC day
• Most queries are only for a small subset of the data

An Example of Hierarchically Organized Table
Real estate information can be naturally modeled by
case class Address(houseNumber: Int,
streetAddress: String,
city: String,
state: String,
zipCode: String)
case class Facts(price: Int,
size: Int,
yearBuilt: Int)
case class School(name: String)
case class Home(address: Address,
facts: Facts,
schools: List[School])

sql("select address.city from homes where facts.price > 2000000”)
.explain(true)
Find cities with houses worth more than 2M
== Physical Plan ==
*(1) Project [address#55.city AS city#75]
+- *(1) Filter (isnotnull(facts#56) && (facts#56.price > 2000000))
+- *(1) FileScan parquet [address#55,facts#56],
DataFilters: [isnotnull(facts#56), (facts#56.price > 2000000)],
Format: Parquet,
PushedFilters: [IsNotNull(facts)],
ReadSchema: struct<address:struct<houseNumber:int,streetAddress:string,
city:string,state:string,zipCode:strin…,
facts:struct(address:int…)>
• We only need two nested columns, address.city and facts.prices
• But entire address and facts are read

[SPARK-4502], [SPARK-25363] Parquet with Nested Columns
• Parquet is a columnar storage format with complex nested data
structures in mind
• Support very efficient compression and encoding schemes
• As a columnar format, each nested column is stored separately as if it's a
flattened table
• No easy way to cherry pick couple nested columns in Spark
• Foundation - Allow reading subset of nested columns right after Parquet
FileScan

== Physical Plan ==
+- *(1) FileScan parquet [address#55,facts#56]
Format: Parquet,
ReadSchema: struct<address:struct<city:string>,facts:struct<price:int>>
• Only two nested columns are read!
• With [SPARK-4502], [SPARK-25363]

== Physical Plan ==
Format: Parquet,
• Parquet predicate pushdown are not working for nested fields in Spark
• With [SPARK-4502], [SPARK-25363]

== Physical Plan ==
Format: Parquet,
PushedFilters: [IsNotNull(facts), GreaterThan(facts.price,2000000)],
• Predicate Pushdown in Parquet for nested fields provides significant
performance gain by eliminating non-matches earlier to read less data
and save the cost of processing them
• With [SPARK-25556]

val areaUdf = udf{ (city: String, state: String, zipCode: String) =>
s"$city, $state $zipCode"
}
val query = sql("select * from homes").repartition(1).select(
areaUdf(col("address.city"),
col("address.state"),
col("address.zipCode"))
).explain()
Applying an UDF after repartition
== Physical Plan ==
*(2) Project [UDF(address#58.city, address#58.state, address#58.zipCode) AS
UDF(address.city, address.state, address.zipCode)#70]
+- Exchange RoundRobinPartitioning(1)
+- *(1) Project [address#58]
+- *(1) FileScan parquet [address#58] Format: Parquet,
ReadSchema: struct<address:struct<houseNumber:int,streetAddress:string,
city:string,state:string,zipCode:string>>

Problems in Supporting Nested Structures in Spark
• Root level columns are represented by Attribute which is base of
leaf named expressions
• To get a nested field from a root level column, a GetStructField
expression with child of Attribute has to be used
• All column pruning logics are done in Attribute levels, resulting
either the entire root column is taken or pruned
• No easy way to cherry pick couple nested columns in this model

[SPARK-25603] Generalize Nested Column Pruning
• [SPARK-4502], [SPARK-25363] are foundation to support nested
structures better with Parquet in Spark
• If an operator such as Repartition, Sample, or Limit are applied after
Parquet FileScan, nested column pruning will not work
• We address this by flattening the nested fields using Alias right after data
read

val query = sql("select * from homes").repartition(1).select(
areaUdf(col("address.city"),
col("address.state"),
col("address.zipCode"))
).explain()
Applying an UDF after repartition
== Physical Plan ==
*(2) Project [UDF(_gen_alias_84#84, _gen_alias_85#85, _gen_alias_86#86) AS UDF(address.city,
address.state, address.zipCode)#64]
+- Exchange RoundRobinPartitioning(1)
+- *(1) Project [address#55.city AS _gen_alias_84#84, address#55.state AS _gen_alias_85#85,
address#55.zipCode AS _gen_alias_86#86]
+- *(1) FileScan parquet [address#55]
ReadSchema: struct<address:struct<city:string,state:string,zipCode:string>>
• Nested fields are replaced by Alias with flatten structures
• Only three used nested fields are read from Parquet files

Production Query - Finding a Needle in a Haystack

Spark 2.3.1
1.2h 7.1TB

Spark 2.4 with [SPARK-4502], [SPARK-25363], and [SPARK-25556]
3.3min 840GB
7.1TB1.2h

• 21x faster in wall clock time 
• 8x less data being read 
• More power efficient

Other work
• Enhance the Dataset performance by analyzing JVM bytecode
and turn closures into Catalyst expressions
• Please check our other presentation tomorrow at 11am for more

Conclusions
With some work, engineering rigor and some optimizations
Spark can run at very large scale in lightning speed

• [SPARK-4502]
• [SPARK-25363]
• [SPARK-25556]
• [SPARK-25603]

Thank you

Making Nested Columns as First Citizen in Apache Spark SQL

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