# imports
from pyspark import SparkContext
from pyspark.sql import HiveContext
# start Spark and Hive SQL contexts
sc = SparkContext("local", "demo app")
hc = HiveContext(sc)
# get the table
print "Printing tables in DB:"
print hc.tableNames()
print "Printing first 10 rows in zip_neighborhood_borough_xref table."
sqlQuery = "SELECT * FROM zip_neighborhood_borough_xref limit 10"
hc.sql(sqlQuery).show()
def sql_hive_context_example(spark):
# create hive context object.
hive_ctx = HiveContext(spark.sparkContext)
# createDataFrame
l = [('Alice', 18), ('Bob', 20), ('Charley', 22)]
df = hive_ctx.createDataFrame(l, ('name', 'age'))
print("createDataFrame API finished")
# registerDataFrameAsTable
hive_ctx.registerDataFrameAsTable(df, "table1")
print("registerDataFrameAsTable API finished")
# sql
tmp_df = hive_ctx.sql("select * from table1")
tmp_df.show()
print("sql API finished")
# table
tmp_df = hive_ctx.table("table1")
tmp_df.show()
print("table API finished")
# tableNames
table_names = hive_ctx.tableNames()
print(table_names)
print("tableNames API finished")
# tables
tables = hive_ctx.tables()
print(tables)
print("tables API finished")
# range
tmp_df = hive_ctx.range(1,10,2)
tmp_df.show()
print("range API finished")
# dropTempTable
hive_ctx.dropTempTable("table1")
table_names = hive_ctx.tableNames()
print(table_names)
print("dropTempTable API finished")
# cacheTable & uncacheTable & clearCache
df = hive_ctx.range(1,10,2)
hive_ctx.registerDataFrameAsTable(df, "table")
hive_ctx.cacheTable("table")
hive_ctx.uncacheTable("table")
hive_ctx.clearCache()
print("cacheTable & uncacheTable & clearCache API finished")
# createExternalTable
# newSession
# registerFunction
# Deprecated in 2.3.0. Use :func:`spark.udf.register` instead
# registerJavaFunction
# Deprecated in 2.3.0. Use :func:`spark.udf.registerJavaFunction` instead
# setConf & getConf
hive_ctx.setConf("key1", "value1")
value = hive_ctx.getConf("key1")
print(value)
print("setConf & getConf API finished")
# refreshTable
# Exception: An error occurred while calling o26.refreshTable:
# Method refreshTable([class java.lang.String]) does not exist
print("Finish running HiveContext API")
from pyspark import SparkContext
from pyspark.sql import HiveContext
from pyspark.sql.types import *
from udf.pyspark.udfs import *
if __name__ == "__main__":
sc = SparkContext(appName="SparkSQL:[demo][pysparkdemo]")
sqlContext = HiveContext(sc)
# RDD is created from a list of rows
df = sqlContext.read.parquet("/mvad/warehouse/session/dspan/date=2015-09-01/")
df.registerTempTable("sessionlog")
for table in sqlContext.tableNames():
print table
df.printSchema()
sqlContext.udf.register("toNormalCookie",toNormalCookie )
sql1 = """ select toNormalCookie(cookie) as cookiestr,eventTime,eventType,geoInfo.country as country,
geoInfo.province as province from sessionlog limit 10 """.replace('\n',' ')
sample = sqlContext.sql(sql1)
sample.show()
sql2 = """select eventType, count(cookie) as count from sessionlog
group by eventType """.replace('\n',' ')
result = sqlContext.sql(sql2)
result.cache()
# only show 20 records
from pyspark import SparkContext
from pyspark.sql import HiveContext
from pyspark.sql.types import Row, StructField, StructType, StringType, IntegerType
if __name__ == "__main__":
sc = SparkContext(appName="SparkSQL:[com.mvad.spark.demo][pysparkdemo]")
sqlContext = HiveContext(sc)
# RDD is created from a list of rows
df = sqlContext.parquetFile(
"/mvad/warehouse/session/dspan/date=2015-05-01/")
df.registerTempTable("sessionlog")
for table in sqlContext.tableNames():
print table
df.printSchema()
sqlContext.udf.register("intarr2str",
lambda array: "".join(map(str, array)))
sql1 = """ select intarr2str(cookie) as cookiestr,eventTime,eventType,geoInfo.country as country,
geoInfo.province as province from sessionlog limit 10 """.replace(
'\n', ' ')
sample = sqlContext.sql(sql1)
sample.show()
sql2 = """select eventType, count(cookie) as count from sessionlog
group by eventType """.replace('\n', ' ')
result = sqlContext.sql(sql2)
result.cache()
# only show 20 records
result.show()
spark_df = sqlCtx.read.format('com.databricks.spark.csv').options(header='true', inferschema='true').load("./data/clicks_test.csv")
spark_df.registerTempTable("clicks_train")
try:
sqlCtx.sql("drop table table_7")
except pyspark.sql.utils.AnalysisException as e:
pass
except Exception as e:
pass
print("*** CREATING TABLE 7 ***")
# table 7 is train but geo location needs to be coded.
# I don't know how to coded in sql so I will just remove it for now
sqlCtx.sql("create table table_7 as select a.document_id, a.platform, "
"a.traffic_source, a.display_id, a.source_id, a.publisher_id, "
"a.category_id, b.ad_id, a.topic_id from table_6 a inner join clicks_train b on a.display_id = b.display_id")
print("*** FINISHED CREATING TABLE 7 ***")
# create train file from table_7
train_spark_df = sqlCtx.sql("select * from table_7")
train_spark_df.write.csv('./cleaned_data/test_files_from_spark')
# TODO do test file
spark_tables = sqlCtx.tableNames()
print(spark_tables)
