Pivoting Data with SparkSQL by Andrew Ray

© 2016 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED.
@SVDataScience | 1
Pivoting Data with SparkSQL
Andrew Ray
Senior Data Engineer
Silicon Valley Data Science

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CODE
git.io/vgy34
(github.com/silicon-valley-data-science/spark-pivot-examples)

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OUTLINE
• What’s a Pivot?
• Syntax
• Real world examples
• Tips and Tricks
• Implementation
• Future work
git.io/vgy34

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WHAT’S A PIVOT?

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WHAT’S A PIVOT?

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WHAT’S A PIVOT?
Group by A, pivot on B, and sum C
A B C
G X 1
G Y 2
G X 3
H Y 4
H Z 5
A X Y Z
G 4 2
H 4 5

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WHAT’S A PIVOT?
Group by A and B
Pivot on BA B C
G X 1
G Y 2
G X 3
H Y 4
H Z 5
A B C
G X 4
G Y 2
H Y 4
H Z 5
A X Y Z
G 4 2
H 4 5

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WHAT’S A PIVOT?
Pivot on B (w/o agg.)
Group by AA B C
G X 1
G Y 2
G X 3
H Y 4
H Z 5
A X Y Z
G 1
G 2
G 3
H 4
H 5
A X Y Z
G 4 2
H 4 5

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SYNTAX

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SYNTAX
• Dataframe/table with columns A, B, C, and D.
• How to
– group by A and B
– pivot on C (with distinct values “small” and “large”)
– sum of D

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SYNTAX: COMPETITION
• pandas (Python)
– pivot_table(df, values='D', index=['A', 'B'],
columns=['C'], aggfunc=np.sum)
• reshape2 (R)
– dcast(df, A + B ~ C, sum)
• Oracle 11g
– SELECT * FROM df PIVOT (sum(D) FOR C IN
('small', 'large')) p

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SYNTAX: SPARKSQL
• Simple
– df.groupBy("A", "B").pivot("C").sum("D")
• Explicit pivot values
– df.groupBy("A", "B")
.pivot("C", Seq("small", "large"))
.sum("D")

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PIVOT
• Added to DataFrame API in Spark 1.6
– Scala
– Java
– Python
– Not R L

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REAL WORLD EXAMPLES

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EXAMPLE 1: REPORTING
• Retail sales
• TPC-DS dataset
– scale factor 1
• Docker image:
docker run -it svds/spark-pivot-reporting
TPC Benchmark™ DS - Standard Specification, Version 2.1.0 Page 18 of 135
2.2.2.3 The implementation chosen by the test sponsor for a particular datatype definition shall be applied consistently
to all the instances of that datatype definition in the schema, except for identifier columns, whose datatype may
be selected to satisfy database scaling requirements.
2.2.3 NULLs
If a column definition includes an ‘N’ in the NULLs column this column is populated in every row of the table
for all scale factors. If the field is blank this column may contain NULLs.
2.2.4 Foreign Key
If the values in this column join with another column, the foreign columns name is listed in the Foreign Key
field of the column definition.
2.3 Fact Table Definitions
2.3.1 Store Sales (SS)
2.3.1.1 Store Sales ER-Diagram
2.3.1.2 Store Sales Column Definitions
Each row in this table represents a single lineitem for a sale made through the store channel and recorded in the
store_sales fact table.
Table 2-1 Store_sales Column Definitions
Column Datatype NULLs Primary Key Foreign Key
ss_sold_date_sk identifier d_date_sk
ss_sold_time_sk identifier t_time_sk
ss_item_sk (1) identifier N Y i_item_sk
ss_customer_sk identifier c_customer_sk
ss_cdemo_sk identifier cd_demo_sk
ss_hdemo_sk identifier hd_demo_sk
ss_addr_sk identifier ca_address_sk
ss_store_sk identifier s_store_sk
ss_promo_sk identifier p_promo_sk

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SALES BY CATEGORY AND QUARTER
sql("""select *, concat('Q', d_qoy) as qoy
from store_sales
join date_dim on ss_sold_date_sk = d_date_sk
join item on ss_item_sk = i_item_sk""")
.groupBy("i_category")
.pivot("qoy")
.agg(round(sum("ss_sales_price")/1000000,2))
.show

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SALES BY CATEGORY AND QUARTER
+-----------+----+----+----+----+
| i_category| Q1| Q2| Q3| Q4|
+-----------+----+----+----+----+
| Books|1.58|1.50|2.84|4.66|
| Women|1.41|1.36|2.54|4.16|
| Music|1.50|1.44|2.66|4.36|
| Children|1.54|1.46|2.74|4.51|
| Sports|1.47|1.40|2.62|4.30|
| Shoes|1.51|1.48|2.68|4.46|
| Jewelry|1.45|1.39|2.59|4.25|
| null|0.04|0.04|0.07|0.13|
|Electronics|1.56|1.49|2.77|4.57|
| Home|1.57|1.51|2.79|4.60|
| Men|1.60|1.54|2.86|4.71|
+-----------+----+----+----+----+

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EXAMPLE 2: FEATURE GENERATION
• MovieLens 1M Dataset
– ~1M movie ratings
– 6040 users
– 3952 movies
• Predict gender based on ratings
– Using 100 most popular movies

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LOAD RATINGS
val ratings_raw = sc.textFile("Downloads/ml-1m/ratings.dat")
case class Rating(user: Int, movie: Int, rating: Int)
val ratings = ratings_raw.map(_.split("::").map(_.toInt)).map(r => Rating(r(0),r(1),r(2))).toDF
ratings.show
+----+-----+------+
|user|movie|rating|
+----+-----+------+
| 11| 1753| 4|
| 11| 1682| 1|
| 11| 216| 4|
| 11| 2997| 4|
| 11| 1259| 3|
...

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LOAD USERS
val users_raw = sc.textFile("Downloads/ml-1m/users.dat")
case class User(user: Int, gender: String, age: Int)
val users = users_raw.map(_.split("::")).map(u => User(u(0).toInt, u(1), u(2).toInt)).toDF
val sample_users = users.where(expr("gender = 'F' or ( rand() * 5 < 2 )"))
sample_users.groupBy("gender").count().show
+------+-----+
|gender|count|
+------+-----+
| F| 1709|
| M| 1744|
+------+-----+

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PREP DATA
val popular = ratings.groupBy("movie")
.count()
.orderBy($"count".desc)
.limit(100)
.map(_.get(0)).collect
val ratings_pivot = ratings.groupBy("user")
.pivot("movie", popular.toSeq)
.agg(expr("coalesce(first(rating),3)").cast("double"))
ratings_pivot.where($"user" === 11).show
+----+----+---+----+----+---+----+---+----+----+---+...
|user|2858|260|1196|1210|480|2028|589|2571|1270|593|...
+----+----+---+----+----+---+----+---+----+----+---+...
| 11| 5.0|3.0| 3.0| 3.0|4.0| 3.0|3.0| 3.0| 3.0|5.0|...
+----+----+---+----+----+---+----+---+----+----+---+...

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BUILD MODEL
val data = ratings_pivot.join(sample_users, "user")
.withColumn("label", expr("if(gender = 'M', 1, 0)").cast("double"))
val assembler = new VectorAssembler()
.setInputCols(popular.map(_.toString))
.setOutputCol("features")
val lr = new LogisticRegression()
val pipeline = new Pipeline().setStages(Array(assembler, lr))
val Array(training, test) = data.randomSplit(Array(0.9, 0.1), seed = 12345)
val model = pipeline.fit(training)

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TEST
val res = model.transform(test).select("label", "prediction")
res.groupBy("label").pivot("prediction", Seq(1.0, 0.0)).count().show
+-----+---+---+
|label|1.0|0.0|
+-----+---+---+
| 1.0|114| 74|
| 0.0| 46|146|
+-----+---+---+
Accuracy 68%

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TIPS AND TRICKS

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USAGE NOTES
• Specify the distinct values of the pivot column
– Otherwise it does this:
val values = df.select(pivotColumn)
.distinct()
.sort(pivotColumn)
.map(_.get(0))
.take(maxValues + 1)
.toSeq

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MULTIPLE AGGREGATIONS
df.groupBy("A", "B").pivot("C").agg(sum("D"), avg("D")).show
+---+---+------------+------------+------------+------------+
| A| B|small_sum(D)|small_avg(D)|large_sum(D)|large_avg(D)|
+---+---+------------+------------+------------+------------+
|foo|two| 6| 3.0| null| null|
|bar|two| 6| 6.0| 7| 7.0|
|foo|one| 1| 1.0| 4| 2.0|
|bar|one| 5| 5.0| 4| 4.0|
+---+---+------------+------------+------------+------------+

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PIVOT MULTIPLE COLUMNS
• Merge columns and pivot as usual
df.withColumn(“p”, concat($”p1”, $”p2”))
.groupBy(“a”, “b”)
.pivot(“p”)
.agg(…)

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MAX COLUMNS
• spark.sql.pivotMaxValues
– Default: 10,000
– When doing a pivot without specifying values for the pivot
column this is the maximum number of (distinct) values
that will be collected without error.

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IMPLEMENTATION

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PIVOT IMPLEMENTATION
• pivot is a method of GroupedData and returns
GroupedData with PivotType.
• New logical operator:
o.a.s.sql.catalyst.plans.logical.Pivot
• Analyzer rule:
o.a.s.sql.catalyst.analysis.Analyzer.ResolvePivot
– Currently translates logical pivot into an aggregation with
lots of if statements.

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FUTURE WORK

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FUTURE WORK
• Add to R API
• Add to SQL syntax
• Add support for unpivot
• Faster implementation

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Pivoting Data with SparkSQL
Andrew Ray
andrew@svds.com
We’re hiring!
svds.com/careers
THANK YOU.
git.io/vgy34

Pivoting Data with SparkSQL by Andrew Ray

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