TPC-DS table primary key constraint not enforced during data generation about spark-sql-perf

[twdsilva]

@juliuszsompolski
The store_sales table defines a composite primary key of (ss_item_sk, ss_ticket_number). The data was indeed generated correctly. Sorry for the noise.

scala> val res = sql("SELECT distinct count(ss_item_sk, ss_ticket_number) FROM store_sales")
res: org.apache.spark.sql.DataFrame = [count(ss_item_sk, ss_ticket_number): bigint]

scala> res.show
+-----------------------------------+                                           
|count(ss_item_sk, ss_ticket_number)|
+-----------------------------------+
|                            2879789|
+-----------------------------------+

[juliuszsompolski]

Please share how you generate data.
Do you use TPCDS kit from https://github.com/databricks/tpcds-kit and follow https://github.com/databricks/spark-sql-perf#setup-a-benchmark ?

[twdsilva]

@juliuszsompolski Thanks for you response. I did use the TPC-DS kit and followed the instructions listed on "set up a benchmark". I have attached the commands that I ran. I noticed this because I tried to write the data to a Phoenix table and ended up with less number of rows than expected. I will rerun the benchmark and see if I still see the issue, or try and repro in a test.

import com.databricks.spark.sql.perf.tpcds.TPCDSTables
import org.apache.spark.SparkContext
import org.apache.spark.sql.SQLContext

val sqlContext = new SQLContext(sc)

// Set:
val rootDir = "/tpch-ds-data"
val databaseName = "TPCH"
val scaleFactor = "1"
val format = "parquet"
// Run:
val tables = new TPCDSTables(sqlContext,
    dsdgenDir = "/tmp/tpcds-kit/tools", // location of dsdgen
    scaleFactor = scaleFactor,
    useDoubleForDecimal = false, // true to replace DecimalType with DoubleType
    useStringForDate = false) // true to replace DateType with StringType


tables.genData(
    location = rootDir,
    format = format,
    overwrite = true, // overwrite the data that is already there
    partitionTables = true, // create the partitioned fact tables 
    clusterByPartitionColumns = true, // shuffle to get partitions coalesced into single files. 
    filterOutNullPartitionValues = false, // true to filter out the partition with NULL key value
    tableFilter = "", // "" means generate all tables
    numPartitions = 100) // how many dsdgen partitions to run - number of input tasks.

// Create the specified database
sql(s"create database $databaseName")
// Create metastore tables in a specified database for your data.
// Once tables are created, the current database will be switched to the specified database.
tables.createExternalTables(rootDir, "parquet", databaseName, overwrite = true, discoverPartitions = true)
// Or, if you want to create temporary tables
// tables.createTemporaryTables(location, format)

// For CBO only, gather statistics on all columns:
tables.analyzeTables(databaseName, analyzeColumns = true) 

// write to phoenix table
 val df = sql("SELECT * FROM store_sales")
df.write.format("org.apache.phoenix.spark").mode("overwrite").option("table", "store_sales"").option("zkUrl", "m1:2181").save()

[juliuszsompolski]

Right,
I did not look fully awake at your issue yesterday :-).
ss_item_sk is the foreign key to the item table. store_sales is a denormalized orders-lineitem table, where ss_ticket_number is the order key, and there can be multiple items on an order, hence the item key and order key together form the key.