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ffm-baseline
Commits
b1bf2771
Commit
b1bf2771
authored
Apr 25, 2019
by
张彦钊
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修改测试文件
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06231a9c
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multi.py
tensnsorflow/multi.py
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tensnsorflow/multi.py
View file @
b1bf2771
...
@@ -22,24 +22,24 @@ def test():
...
@@ -22,24 +22,24 @@ def test():
spark
=
SparkSession
.
builder
.
config
(
conf
=
sparkConf
)
.
enableHiveSupport
()
.
getOrCreate
()
spark
=
SparkSession
.
builder
.
config
(
conf
=
sparkConf
)
.
enableHiveSupport
()
.
getOrCreate
()
spark
.
sql
(
"use online"
)
spark
.
sql
(
"use online"
)
spark
.
sql
(
"ADD JAR hdfs:///user/hive/share/lib/udf/brickhouse-0.7.1-SNAPSHOT.jar"
)
#
spark.sql("ADD JAR hdfs:///user/hive/share/lib/udf/brickhouse-0.7.1-SNAPSHOT.jar")
spark
.
sql
(
"ADD JAR hdfs:///user/hive/share/lib/udf/hive-udf-1.0-SNAPSHOT.jar"
)
#
spark.sql("ADD JAR hdfs:///user/hive/share/lib/udf/hive-udf-1.0-SNAPSHOT.jar")
#
spark.sql("ADD JAR /srv/apps/brickhouse-0.7.1-SNAPSHOT.jar")
spark
.
sql
(
"ADD JAR /srv/apps/brickhouse-0.7.1-SNAPSHOT.jar"
)
#
spark.sql("ADD JAR /srv/apps/hive-udf-1.0-SNAPSHOT.jar")
spark
.
sql
(
"ADD JAR /srv/apps/hive-udf-1.0-SNAPSHOT.jar"
)
spark
.
sql
(
"CREATE TEMPORARY FUNCTION json_map AS 'brickhouse.udf.json.JsonMapUDF'"
)
spark
.
sql
(
"CREATE TEMPORARY FUNCTION json_map AS 'brickhouse.udf.json.JsonMapUDF'"
)
spark
.
sql
(
"CREATE TEMPORARY FUNCTION is_json AS 'com.gmei.hive.common.udf.UDFJsonFormatCheck'"
)
spark
.
sql
(
"CREATE TEMPORARY FUNCTION is_json AS 'com.gmei.hive.common.udf.UDFJsonFormatCheck'"
)
sql
=
"select user_id from online.tl_hdfs_maidian_view where partition_date = '20190412' limit 10"
sql
=
"select user_id from online.tl_hdfs_maidian_view where partition_date = '20190412' limit 10"
spark
.
sql
(
sql
)
.
show
(
6
)
spark
.
sql
(
sql
)
.
show
(
6
)
#
ti = pti.TiContext(spark)
ti
=
pti
.
TiContext
(
spark
)
#
ti.tidbMapDatabase("jerry_test")
ti
.
tidbMapDatabase
(
"jerry_test"
)
#
#
spark.sparkContext.setLogLevel("WARN")
spark
.
sparkContext
.
setLogLevel
(
"WARN"
)
#
df = spark.sql("select max(stat_date) from esmm_train_data")
df
=
spark
.
sql
(
"select max(stat_date) from esmm_train_data"
)
#
df.show()
df
.
show
()
#
t = df.rdd.map(lambda x: str(x[0])).collect()
t
=
df
.
rdd
.
map
(
lambda
x
:
str
(
x
[
0
]))
.
collect
()
#
print(t)
print
(
t
)
# data = [(0, 18.0), (1, 19.0), (2, 8.0), (3, 5.0), (4, 2.2), (5, 9.2), (6, 14.4)]
# data = [(0, 18.0), (1, 19.0), (2, 8.0), (3, 5.0), (4, 2.2), (5, 9.2), (6, 14.4)]
...
...
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