我正在执行以下代码：
from pyspark.sql import types as T, functions as F, SparkSession
spark = SparkSession.builder.getOrCreate()
schema = T.StructType([
T.StructField("col_1", T.IntegerType(), False),
T.StructField("col_2", T.IntegerType(), False),
T.StructField("measure_1", T.FloatType(), False),
T.StructField("measure_2", T.FloatType(), False),
])
data = [
{"col_1": 1, "col_2": 2, "measure_1": 0.5, "measure_2": 1.5},
{"col_1": 2, "col_2": 3, "measure_1": 2.5, "measure_2": 3.5}
]
df = spark.createDataFrame(data, schema)
df.show()
"""
+-----+-----+---------+---------+
|col_1|col_2|measure_1|measure_2|
+-----+-----+---------+---------+
| 1| 2| 0.5| 1.5|
| 2| 3| 2.5| 3.5|
+-----+-----+---------+---------+
"""
group_cols = ["col_1", "col_2"]
measure_cols = ["measure_1", "measure_2"]
for col in measure_cols:
stats = df.groupBy(group_cols).agg(
F.max(col).alias("max_" + col),
F.avg(col).alias("avg_" + col),
)
df = df.join(stats, group_cols)
df.show()
"""
+-----+-----+---------+---------+-------------+-------------+-------------+-------------+
|col_1|col_2|measure_1|measure_2|max_measure_1|avg_measure_1|max_measure_2|avg_measure_2|
+-----+-----+---------+---------+-------------+-------------+-------------+-------------+
| 2| 3| 2.5| 3.5| 2.5| 2.5| 3.5| 3.5|
| 1| 2| 0.5| 1.5| 0.5| 0.5| 1.5| 1.5|
+-----+-----+---------+---------+-------------+-------------+-------------+-------------+
"""
现在，如果我的初始df不是那么简单，而是一系列联接或其他操作，那么问题就出现了。当我查看我的作业时，我注意到df似乎是在执行groupBy操作时派生的。这里的简单查询计划是：
df.explain()
"""
>>> df.explain()
== Physical Plan ==
*(11) Project [col_1#26, col_2#27, measure_1#28, measure_2#29, max_measure_1#56, avg_measure_1#58, max_measure_2#80, avg_measure_2#82]
+- *(11) SortMergeJoin [col_1#26, col_2#27], [col_1#87, col_2#88], Inner
:- *(5) Project [col_1#26, col_2#27, measure_1#28, measure_2#29, max_measure_1#56, avg_measure_1#58]
: +- *(5) SortMergeJoin [col_1#26, col_2#27], [col_1#63, col_2#64], Inner
: :- *(2) Sort [col_1#26 ASC NULLS FIRST, col_2#27 ASC NULLS FIRST], false, 0
: : +- Exchange hashpartitioning(col_1#26, col_2#27, 200), ENSURE_REQUIREMENTS, [id=#276]
: : +- *(1) Scan ExistingRDD[col_1#26,col_2#27,measure_1#28,measure_2#29]
: +- *(4) Sort [col_1#63 ASC NULLS FIRST, col_2#64 ASC NULLS FIRST], false, 0
: +- *(4) HashAggregate(keys=[col_1#63, col_2#64], functions=[max(measure_1#65), avg(cast(measure_1#65 as double))])
: +- Exchange hashpartitioning(col_1#63, col_2#64, 200), ENSURE_REQUIREMENTS, [id=#282]
: +- *(3) HashAggregate(keys=[col_1#63, col_2#64], functions=[partial_max(measure_1#65), partial_avg(cast(measure_1#65 as double))])
: +- *(3) Project [col_1#63, col_2#64, measure_1#65]
: +- *(3) Scan ExistingRDD[col_1#63,col_2#64,measure_1#65,measure_2#66]
+- *(10) Sort [col_1#87 ASC NULLS FIRST, col_2#88 ASC NULLS FIRST], false, 0
+- *(10) HashAggregate(keys=[col_1#87, col_2#88], functions=[max(measure_2#90), avg(cast(measure_2#90 as double))])
+- *(10) HashAggregate(keys=[col_1#87, col_2#88], functions=[partial_max(measure_2#90), partial_avg(cast(measure_2#90 as double))])
+- *(10) Project [col_1#87, col_2#88, measure_2#90]
+- *(10) SortMergeJoin [col_1#87, col_2#88], [col_1#63, col_2#64], Inner
:- *(7) Sort [col_1#87 ASC NULLS FIRST, col_2#88 ASC NULLS FIRST], false, 0
: +- Exchange hashpartitioning(col_1#87, col_2#88, 200), ENSURE_REQUIREMENTS, [id=#293]
: +- *(6) Project [col_1#87, col_2#88, measure_2#90]
: +- *(6) Scan ExistingRDD[col_1#87,col_2#88,measure_1#89,measure_2#90]
+- *(9) Sort [col_1#63 ASC NULLS FIRST, col_2#64 ASC NULLS FIRST], false, 0
+- *(9) HashAggregate(keys=[col_1#63, col_2#64], functions=[])
+- Exchange hashpartitioning(col_1#63, col_2#64, 200), ENSURE_REQUIREMENTS, [id=#299]
+- *(8) HashAggregate(keys=[col_1#63, col_2#64], functions=[])
+- *(8) Project [col_1#63, col_2#64]
+- *(8) Scan ExistingRDD[col_1#63,col_2#64,measure_1#65,measure_2#66]
"""
但是，例如，如果我更改上面的代码，使初始df成为联接和并集的结果：
from pyspark.sql import types as T, functions as F, SparkSession
spark = SparkSession.builder.getOrCreate()
schema = T.StructType([
T.StructField("col_1", T.IntegerType(), False),
T.StructField("col_2", T.IntegerType(), False),
T.StructField("measure_1", T.FloatType(), False),
T.StructField("measure_2", T.FloatType(), False),
])
data = [
{"col_1": 1, "col_2": 2, "measure_1": 0.5, "measure_2": 1.5},
{"col_1": 2, "col_2": 3, "measure_1": 2.5, "measure_2": 3.5}
]
df = spark.createDataFrame(data, schema)
right_schema = T.StructType([
T.StructField("col_1", T.IntegerType(), False)
])
right_data = [
{"col_1": 1},
{"col_1": 1},
{"col_1": 2},
{"col_1": 2}
]
right_df = spark.createDataFrame(right_data, right_schema)
df = df.unionByName(df)
df = df.join(right_df, on="col_1")
df.show()
"""
+-----+-----+---------+---------+
|col_1|col_2|measure_1|measure_2|
+-----+-----+---------+---------+
| 1| 2| 0.5| 1.5|
| 1| 2| 0.5| 1.5|
| 1| 2| 0.5| 1.5|
| 1| 2| 0.5| 1.5|
| 2| 3| 2.5| 3.5|
| 2| 3| 2.5| 3.5|
| 2| 3| 2.5| 3.5|
| 2| 3| 2.5| 3.5|
+-----+-----+---------+---------+
"""
df.explain()
"""
== Physical Plan ==
*(7) Project [col_1#299, col_2#300, measure_1#301, measure_2#302, col_2#354, measure_1#355, measure_2#356]
+- *(7) SortMergeJoin [col_1#299], [col_1#353], Inner
:- *(3) Sort [col_1#299 ASC NULLS FIRST], false, 0
: +- Exchange hashpartitioning(col_1#299, 200), ENSURE_REQUIREMENTS, [id=#595]
: +- Union
: :- *(1) Scan ExistingRDD[col_1#299,col_2#300,measure_1#301,measure_2#302]
: +- *(2) Scan ExistingRDD[col_1#299,col_2#300,measure_1#301,measure_2#302]
+- *(6) Sort [col_1#353 ASC NULLS FIRST], false, 0
+- ReusedExchange [col_1#353, col_2#354, measure_1#355, measure_2#356], Exchange hashpartitioning(col_1#299, 200), ENSURE_REQUIREMENTS, [id=#595]
"""
group_cols = ["col_1", "col_2"]
measure_cols = ["measure_1", "measure_2"]
for col in measure_cols:
stats = df.groupBy(group_cols).agg(
F.max(col).alias("max_" + col),
F.avg(col).alias("avg_" + col),
)
df = df.join(stats, group_cols)
df.show()
"""
+-----+-----+---------+---------+-------------+-------------+-------------+-------------+
|col_1|col_2|measure_1|measure_2|max_measure_1|avg_measure_1|max_measure_2|avg_measure_2|
+-----+-----+---------+---------+-------------+-------------+-------------+-------------+
| 2| 3| 2.5| 3.5| 2.5| 2.5| 3.5| 3.5|
| 2| 3| 2.5| 3.5| 2.5| 2.5| 3.5| 3.5|
| 2| 3| 2.5| 3.5| 2.5| 2.5| 3.5| 3.5|
| 2| 3| 2.5| 3.5| 2.5| 2.5| 3.5| 3.5|
| 1| 2| 0.5| 1.5| 0.5| 0.5| 1.5| 1.5|
| 1| 2| 0.5| 1.5| 0.5| 0.5| 1.5| 1.5|
| 1| 2| 0.5| 1.5| 0.5| 0.5| 1.5| 1.5|
| 1| 2| 0.5| 1.5| 0.5| 0.5| 1.5| 1.5|
+-----+-----+---------+---------+-------------+-------------+-------------+-------------+
"""
df.explain()
"""
== Physical Plan ==
*(31) Project [col_1#404, col_2#405, measure_1#406, measure_2#407, max_measure_1#465, avg_measure_1#467, max_measure_2#489, avg_measure_2#491]
+- *(31) SortMergeJoin [col_1#404, col_2#405], [col_1#496, col_2#497], Inner
:- *(15) Project [col_1#404, col_2#405, measure_1#406, measure_2#407, max_measure_1#465, avg_measure_1#467]
: +- *(15) SortMergeJoin [col_1#404, col_2#405], [col_1#472, col_2#473], Inner
: :- *(7) Sort [col_1#404 ASC NULLS FIRST, col_2#405 ASC NULLS FIRST], false, 0
: : +- Exchange hashpartitioning(col_1#404, col_2#405, 200), ENSURE_REQUIREMENTS, [id=#1508]
: : +- *(6) Project [col_1#404, col_2#405, measure_1#406, measure_2#407]
: : +- *(6) SortMergeJoin [col_1#404], [col_1#412], Inner
: : :- *(3) Sort [col_1#404 ASC NULLS FIRST], false, 0
: : : +- Exchange hashpartitioning(col_1#404, 200), ENSURE_REQUIREMENTS, [id=#1494]
: : : +- Union
: : : :- *(1) Scan ExistingRDD[col_1#404,col_2#405,measure_1#406,measure_2#407]
: : : +- *(2) Scan ExistingRDD[col_1#404,col_2#405,measure_1#406,measure_2#407]
: : +- *(5) Sort [col_1#412 ASC NULLS FIRST], false, 0
: : +- Exchange hashpartitioning(col_1#412, 200), ENSURE_REQUIREMENTS, [id=#1500]
: : +- *(4) Scan ExistingRDD[col_1#412]
: +- *(14) Sort [col_1#472 ASC NULLS FIRST, col_2#473 ASC NULLS FIRST], false, 0
: +- Exchange hashpartitioning(col_1#472, col_2#473, 200), ENSURE_REQUIREMENTS, [id=#1639]
: +- *(13) HashAggregate(keys=[col_1#472, col_2#473], functions=[max(measure_1#474), avg(cast(measure_1#474 as double))])
: +- *(13) HashAggregate(keys=[col_1#472, col_2#473], functions=[partial_max(measure_1#474), partial_avg(cast(measure_1#474 as double))])
: +- *(13) Project [col_1#472, col_2#473, measure_1#474]
: +- *(13) SortMergeJoin [col_1#472], [col_1#412], Inner
: :- *(10) Sort [col_1#472 ASC NULLS FIRST], false, 0
: : +- Exchange hashpartitioning(col_1#472, 200), ENSURE_REQUIREMENTS, [id=#1516]
: : +- Union
: : :- *(8) Project [col_1#472, col_2#473, measure_1#474]
: : : +- *(8) Scan ExistingRDD[col_1#472,col_2#473,measure_1#474,measure_2#475]
: : +- *(9) Project [col_1#472, col_2#473, measure_1#474]
: : +- *(9) Scan ExistingRDD[col_1#472,col_2#473,measure_1#474,measure_2#475]
: +- *(12) Sort [col_1#412 ASC NULLS FIRST], false, 0
: +- ReusedExchange [col_1#412], Exchange hashpartitioning(col_1#412, 200), ENSURE_REQUIREMENTS, [id=#1500]
+- *(30) Sort [col_1#496 ASC NULLS FIRST, col_2#497 ASC NULLS FIRST], false, 0
+- *(30) HashAggregate(keys=[col_1#496, col_2#497], functions=[max(measure_2#499), avg(cast(measure_2#499 as double))])
+- *(30) HashAggregate(keys=[col_1#496, col_2#497], functions=[partial_max(measure_2#499), partial_avg(cast(measure_2#499 as double))])
+- *(30) Project [col_1#496, col_2#497, measure_2#499]
+- *(30) SortMergeJoin [col_1#496, col_2#497], [col_1#472, col_2#473], Inner
:- *(22) Sort [col_1#496 ASC NULLS FIRST, col_2#497 ASC NULLS FIRST], false, 0
: +- Exchange hashpartitioning(col_1#496, col_2#497, 200), ENSURE_REQUIREMENTS, [id=#1660]
: +- *(21) Project [col_1#496, col_2#497, measure_2#499]
: +- *(21) SortMergeJoin [col_1#496], [col_1#412], Inner
: :- *(18) Sort [col_1#496 ASC NULLS FIRST], false, 0
: : +- Exchange hashpartitioning(col_1#496, 200), ENSURE_REQUIREMENTS, [id=#1544]
: : +- Union
: : :- *(16) Project [col_1#496, col_2#497, measure_2#499]
: : : +- *(16) Scan ExistingRDD[col_1#496,col_2#497,measure_1#498,measure_2#499]
: : +- *(17) Project [col_1#496, col_2#497, measure_2#499]
: : +- *(17) Scan ExistingRDD[col_1#496,col_2#497,measure_1#498,measure_2#499]
: +- *(20) Sort [col_1#412 ASC NULLS FIRST], false, 0
: +- ReusedExchange [col_1#412], Exchange hashpartitioning(col_1#412, 200), ENSURE_REQUIREMENTS, [id=#1500]
+- *(29) Sort [col_1#472 ASC NULLS FIRST, col_2#473 ASC NULLS FIRST], false, 0
+- Exchange hashpartitioning(col_1#472, col_2#473, 200), ENSURE_REQUIREMENTS, [id=#1707]
+- *(28) HashAggregate(keys=[col_1#472, col_2#473], functions=[])
+- *(28) HashAggregate(keys=[col_1#472, col_2#473], functions=[])
+- *(28) Project [col_1#472, col_2#473]
+- *(28) SortMergeJoin [col_1#472], [col_1#412], Inner
:- *(25) Sort [col_1#472 ASC NULLS FIRST], false, 0
: +- Exchange hashpartitioning(col_1#472, 200), ENSURE_REQUIREMENTS, [id=#1566]
: +- Union
: :- *(23) Project [col_1#472, col_2#473]
: : +- *(23) Scan ExistingRDD[col_1#472,col_2#473,measure_1#474,measure_2#475]
: +- *(24) Project [col_1#472, col_2#473]
: +- *(24) Scan ExistingRDD[col_1#472,col_2#473,measure_1#474,measure_2#475]
+- *(27) Sort [col_1#412 ASC NULLS FIRST], false, 0
+- ReusedExchange [col_1#412], Exchange hashpartitioning(col_1#412, 200), ENSURE_REQUIREMENTS, [id=#1500]
"""
您可以在查询计划中看到，join+并集派生了多次，这反映在我的作业执行报告中，在该报告中，我看到具有相同数量任务的阶段一次又一次地运行。
我如何才能阻止这种重新推导的发生？
转换的内部循环，在该循环中，您可以针对基本DataFrame多次联接+派生列，这将受益于PySpark的.cache()函数。这明确指示Spark保留派生的DataFrame，而不是重新计算它。这意味着你将计算一次初始并集+联接，然后在后续转换中重用DataFrame。
这是一个单行添加，将大大有利于您的执行。
from pyspark.sql import types as T, functions as F, SparkSession
spark = SparkSession.builder.getOrCreate()
schema = T.StructType([
T.StructField("col_1", T.IntegerType(), False),
T.StructField("col_2", T.IntegerType(), False),
T.StructField("measure_1", T.FloatType(), False),
T.StructField("measure_2", T.FloatType(), False),
])
data = [
{"col_1": 1, "col_2": 2, "measure_1": 0.5, "measure_2": 1.5},
{"col_1": 2, "col_2": 3, "measure_1": 2.5, "measure_2": 3.5}
]
df = spark.createDataFrame(data, schema)
right_schema = T.StructType([
T.StructField("col_1", T.IntegerType(), False)
])
right_data = [
{"col_1": 1},
{"col_1": 1},
{"col_1": 2},
{"col_1": 2}
]
right_df = spark.createDataFrame(right_data, right_schema)
df = df.unionByName(df)
df = df.join(right_df, on="col_1")
# ========= Added this line BEFORE the loop
df = df.cache()
# =========
group_cols = ["col_1", "col_2"]
measure_cols = ["measure_1", "measure_2"]
for col in measure_cols:
stats = df.groupBy(group_cols).agg(
F.max(col).alias("max_" + col),
F.avg(col).alias("avg_" + col),
)
df = df.join(stats, group_cols)
df.show()
"""
+-----+-----+---------+---------+-------------+-------------+-------------+-------------+
|col_1|col_2|measure_1|measure_2|max_measure_1|avg_measure_1|max_measure_2|avg_measure_2|
+-----+-----+---------+---------+-------------+-------------+-------------+-------------+
| 2| 3| 2.5| 3.5| 2.5| 2.5| 3.5| 3.5|
| 2| 3| 2.5| 3.5| 2.5| 2.5| 3.5| 3.5|
| 2| 3| 2.5| 3.5| 2.5| 2.5| 3.5| 3.5|
| 2| 3| 2.5| 3.5| 2.5| 2.5| 3.5| 3.5|
| 1| 2| 0.5| 1.5| 0.5| 0.5| 1.5| 1.5|
| 1| 2| 0.5| 1.5| 0.5| 0.5| 1.5| 1.5|
| 1| 2| 0.5| 1.5| 0.5| 0.5| 1.5| 1.5|
| 1| 2| 0.5| 1.5| 0.5| 0.5| 1.5| 1.5|
+-----+-----+---------+---------+-------------+-------------+-------------+-------------+
"""
df.explain()
"""
>>> df.explain()
== Physical Plan ==
*(4) Project [col_1#1265, col_2#1266, measure_1#1267, measure_2#1268, max_measure_1#1312, avg_measure_1#1314, max_measure_2#1336, avg_measure_2#1338]
+- *(4) BroadcastHashJoin [col_1#1265, col_2#1266], [col_1#1343, col_2#1344], Inner, BuildRight, false
:- *(4) Project [col_1#1265, col_2#1266, measure_1#1267, measure_2#1268, max_measure_1#1312, avg_measure_1#1314]
: +- *(4) BroadcastHashJoin [col_1#1265, col_2#1266], [col_1#1319, col_2#1320], Inner, BuildLeft, false
: :- BroadcastExchange HashedRelationBroadcastMode(List((shiftleft(cast(input[0, int, false] as bigint), 32) | (cast(input[1, int, false] as bigint) & 4294967295))),false), [id=#2439]
: : +- *(1) ColumnarToRow
: : +- InMemoryTableScan [col_1#1265, col_2#1266, measure_1#1267, measure_2#1268]
: : +- InMemoryRelation [col_1#1265, col_2#1266, measure_1#1267, measure_2#1268], StorageLevel(disk, memory, deserialized, 1 replicas)
: : +- *(6) Project [col_1#1265, col_2#1266, measure_1#1267, measure_2#1268]
: : +- *(6) SortMergeJoin [col_1#1265], [col_1#1273], Inner
: : :- *(3) Sort [col_1#1265 ASC NULLS FIRST], false, 0
: : : +- Exchange hashpartitioning(col_1#1265, 200), ENSURE_REQUIREMENTS, [id=#2169]
: : : +- Union
: : : :- *(1) Scan ExistingRDD[col_1#1265,col_2#1266,measure_1#1267,measure_2#1268]
: : : +- *(2) Scan ExistingRDD[col_1#1265,col_2#1266,measure_1#1267,measure_2#1268]
: : +- *(5) Sort [col_1#1273 ASC NULLS FIRST], false, 0
: : +- Exchange hashpartitioning(col_1#1273, 200), ENSURE_REQUIREMENTS, [id=#2175]
: : +- *(4) Scan ExistingRDD[col_1#1273]
: +- *(4) HashAggregate(keys=[col_1#1319, col_2#1320], functions=[max(measure_1#1321), avg(cast(measure_1#1321 as double))])
: +- *(4) HashAggregate(keys=[col_1#1319, col_2#1320], functions=[partial_max(measure_1#1321), partial_avg(cast(measure_1#1321 as double))])
: +- *(4) ColumnarToRow
: +- InMemoryTableScan [col_1#1319, col_2#1320, measure_1#1321]
: +- InMemoryRelation [col_1#1319, col_2#1320, measure_1#1321, measure_2#1322], StorageLevel(disk, memory, deserialized, 1 replicas)
: +- *(6) Project [col_1#1265, col_2#1266, measure_1#1267, measure_2#1268]
: +- *(6) SortMergeJoin [col_1#1265], [col_1#1273], Inner
: :- *(3) Sort [col_1#1265 ASC NULLS FIRST], false, 0
: : +- Exchange hashpartitioning(col_1#1265, 200), ENSURE_REQUIREMENTS, [id=#2169]
: : +- Union
: : :- *(1) Scan ExistingRDD[col_1#1265,col_2#1266,measure_1#1267,measure_2#1268]
: : +- *(2) Scan ExistingRDD[col_1#1265,col_2#1266,measure_1#1267,measure_2#1268]
: +- *(5) Sort [col_1#1273 ASC NULLS FIRST], false, 0
: +- Exchange hashpartitioning(col_1#1273, 200), ENSURE_REQUIREMENTS, [id=#2175]
: +- *(4) Scan ExistingRDD[col_1#1273]
+- BroadcastExchange HashedRelationBroadcastMode(List((shiftleft(cast(input[0, int, false] as bigint), 32) | (cast(input[1, int, false] as bigint) & 4294967295))),false), [id=#2461]
+- *(3) HashAggregate(keys=[col_1#1343, col_2#1344], functions=[max(measure_2#1346), avg(cast(measure_2#1346 as double))])
+- *(3) HashAggregate(keys=[col_1#1343, col_2#1344], functions=[partial_max(measure_2#1346), partial_avg(cast(measure_2#1346 as double))])
+- *(3) Project [col_1#1343, col_2#1344, measure_2#1346]
+- *(3) BroadcastHashJoin [col_1#1343, col_2#1344], [col_1#1319, col_2#1320], Inner, BuildRight, false
:- *(3) ColumnarToRow
: +- InMemoryTableScan [col_1#1343, col_2#1344, measure_2#1346]
: +- InMemoryRelation [col_1#1343, col_2#1344, measure_1#1345, measure_2#1346], StorageLevel(disk, memory, deserialized, 1 replicas)
: +- *(6) Project [col_1#1265, col_2#1266, measure_1#1267, measure_2#1268]
: +- *(6) SortMergeJoin [col_1#1265], [col_1#1273], Inner
: :- *(3) Sort [col_1#1265 ASC NULLS FIRST], false, 0
: : +- Exchange hashpartitioning(col_1#1265, 200), ENSURE_REQUIREMENTS, [id=#2169]
: : +- Union
: : :- *(1) Scan ExistingRDD[col_1#1265,col_2#1266,measure_1#1267,measure_2#1268]
: : +- *(2) Scan ExistingRDD[col_1#1265,col_2#1266,measure_1#1267,measure_2#1268]
: +- *(5) Sort [col_1#1273 ASC NULLS FIRST], false, 0
: +- Exchange hashpartitioning(col_1#1273, 200), ENSURE_REQUIREMENTS, [id=#2175]
: +- *(4) Scan ExistingRDD[col_1#1273]
+- BroadcastExchange HashedRelationBroadcastMode(List((shiftleft(cast(input[0, int, false] as bigint), 32) | (cast(input[1, int, false] as bigint) & 4294967295))),false), [id=#2454]
+- *(2) HashAggregate(keys=[col_1#1319, col_2#1320], functions=[])
+- *(2) HashAggregate(keys=[col_1#1319, col_2#1320], functions=[])
+- *(2) ColumnarToRow
+- InMemoryTableScan [col_1#1319, col_2#1320]
+- InMemoryRelation [col_1#1319, col_2#1320, measure_1#1321, measure_2#1322], StorageLevel(disk, memory, deserialized, 1 replicas)
+- *(6) Project [col_1#1265, col_2#1266, measure_1#1267, measure_2#1268]
+- *(6) SortMergeJoin [col_1#1265], [col_1#1273], Inner
:- *(3) Sort [col_1#1265 ASC NULLS FIRST], false, 0
: +- Exchange hashpartitioning(col_1#1265, 200), ENSURE_REQUIREMENTS, [id=#2169]
: +- Union
: :- *(1) Scan ExistingRDD[col_1#1265,col_2#1266,measure_1#1267,measure_2#1268]
: +- *(2) Scan ExistingRDD[col_1#1265,col_2#1266,measure_1#1267,measure_2#1268]
+- *(5) Sort [col_1#1273 ASC NULLS FIRST], false, 0
+- Exchange hashpartitioning(col_1#1273, 200), ENSURE_REQUIREMENTS, [id=#2175]
+- *(4) Scan ExistingRDD[col_1#1273]
"""
现在，您可以在查询计划中看到，使用InMemoryTableRelation来代替几个重复的shuffle，并且您的作业执行也会反映出同样的情况。
注意：.cache()不会更改您的查询计划，也不会截断它，它只是更改创建和重新使用数据的方式