[SPARK-22207] High memory usage when converting relational data to Hierarchical data

• Type: Bug
• Status: Resolved
• Priority: Major
• Resolution: Incomplete
• Affects Version/s: 2.1.0
• Component/s: Spark Core
• Labels: bulk-closed
• Assignee: Unassigned
• Reporter: kanika dhuria
• Created: 05/Oct/17 06:20
• Updated: 25/May/21 01:38
• Resolved: 25/May/21 01:38

Description

Have 4 tables
lineitems ~1.4Gb,
orders ~ 330MB
customer ~47MB
nations ~ 2.2K

These tables are related as follows
There are multiple lineitems per order (pk, fk:orderkey)
There are multiple orders per customer(pk,fk: cust_key)
There are multiple customers per nation(pk, fk:nation key)

Data is almost evenly distributed.

Building hierarchy till 3 levels i.e joining lineitems, orders, customers works good with executor memory 4Gb/2cores
Adding nations require 8GB/2 cores or 4GB/1 core memory.

==============================================================

val sqlContext = SparkSession.builder() .enableHiveSupport() .config("spark.sql.retainGroupColumns", false) .config("spark.sql.crossJoin.enabled", true) .getOrCreate()
 
  val orders = sqlContext.sql("select * from orders")
  val lineItem = sqlContext.sql("select * from lineitems")
  
  val customer = sqlContext.sql("select * from customers")
  
  val nation = sqlContext.sql("select * from nations")
  
  val lineitemOrders = lineItem.groupBy(col("l_orderkey")).agg(col("l_orderkey"), collect_list(struct(col("l_partkey"), col("l_suppkey"),col("l_linenumber"),col("l_quantity"),col("l_extendedprice"),col("l_discount"),col("l_tax"),col("l_returnflag"),col("l_linestatus"),col("l_shipdate"),col("l_commitdate"),col("l_receiptdate"),col("l_shipinstruct"),col("l_shipmode"))).as("lineitem")).join(orders, orders("O_ORDERKEY")=== lineItem("l_orderkey")).select(col("O_ORDERKEY"), col("O_CUSTKEY"),  col("O_ORDERSTATUS"), col("O_TOTALPRICE"), col("O_ORDERDATE"), col("O_ORDERPRIORITY"), col("O_CLERK"), col("O_SHIPPRIORITY"), col("O_COMMENT"),  col("lineitem"))  
  
  val customerList = lineitemOrders.groupBy(col("o_custkey")).agg(col("o_custkey"),collect_list(struct(col("O_ORDERKEY"), col("O_CUSTKEY"),  col("O_ORDERSTATUS"), col("O_TOTALPRICE"), col("O_ORDERDATE"), col("O_ORDERPRIORITY"), col("O_CLERK"), col("O_SHIPPRIORITY"), col("O_COMMENT"),col("lineitem"))).as("items")).join(customer,customer("c_custkey")=== lineitemOrders("o_custkey")).select(col("c_custkey"),col("c_name"),col("c_nationkey"),col("items"))


 val nationList = customerList.groupBy(col("c_nationkey")).agg(col("c_nationkey"),collect_list(struct(col("c_custkey"),col("c_name"),col("c_nationkey"),col("items"))).as("custList")).join(nation,nation("n_nationkey")===customerList("c_nationkey")).select(col("n_nationkey"),col("n_name"),col("custList"))
 
  nationList.write.mode("overwrite").json("filePath")

========================================================
If the customeList is saved in a file and then the last agg/join is run separately, it does run fine in 4GB/2 core .

I can provide the data if needed.