kanika dhuria created SPARK-22207:
-------------------------------------
Summary: High memory usage when converting relational data to
Hierarchical data
Key: SPARK-22207
URL: https://issues.apache.org/jira/browse/SPARK-22207
Project: Spark
Issue Type: Bug
Components: Spark Core
Affects Versions: 2.1.0
Reporter: kanika dhuria
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.
==============================================================
{noformat}
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")
{noformat}
========================================================
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.
--
This message was sent by Atlassian JIRA
(v6.4.14#64029)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
