概述
1. 通过Hive view
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CREATE EXTERNAL TABLE
if
not exists finance.json_serde_optd_table (
retCode string,
retMsg string,
data array<struct< secid:string,=
""
tradedate:date,=
""
optid:string,=
""
ticker:string,=
""
secshortname:string,=
""
exchangecd:string,=
""
presettleprice:
double
,=
""
precloseprice:
double
,=
""
openprice:
double
,=
""
highestprice:
double
,=
""
lowestprice:
double
,=
""
closeprice:
double
,=
""
settlprice:
double
,=
""
turnovervol:
double
,=
""
turnovervalue:
double
,=
""
openint:
int
=
""
>>)
ROW FORMAT SERDE
'org.apache.hive.hcatalog.data.JsonSerDe'
LOCATION
'hdfs://wdp.xxxxx.cn:8020/nifi/finance1/optd/'
;
create table
if
not exists finance.tb_optd
as
SELECT b.data.secID,
b.data.tradeDate,
b.data.optID,
b.data.ticker,
b.data.secShortName,
b.data.exchangeCD,
b.data.preSettlePrice,
b.data.preClosePrice,
b.data.openPrice,
b.data.highestPrice,
b.data.lowestPrice,
b.data.closePrice,
b.data.settlPrice,
b.data.turnoverVol,
b.data.turnoverValue,
b.data.openInt
FROM finance.json_serde_optd_table LATERAL VIEW explode(json_serde_optd_table.data) b AS data;
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2. 通过Zeppelin
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%dep
z.load(
"/usr/hdp/2.4.2.0-258/hive-hcatalog/share/hcatalog/hive-hcatalog-core.jar"
);
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// 定义导入的hive对象集合
case
class
HiveConfig(database: String, modelName: String, hdfsPath: String, schema: String, schema_tb: String);
var hiveConfigList = List[HiveConfig]();
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// 创建equd数据结构
// 定义json结构
val schema_json_equd_serde =
""
" retCode string,
retMsg string,
data array<struct< secid=
""
:=
""
string,=
""
tradedate=
""
date,=
""
ticker=
""
secshortname=
""
exchangecd=
""
precloseprice=
""
double
,=
""
actprecloseprice:=
""
openprice=
""
highestprice=
""
lowestprice=
""
closeprice=
""
turnovervol=
""
turnovervalue=
""
dealamount=
""
int
,=
""
turnoverrate=
""
accumadjfactor=
""
negmarketvalue=
""
marketvalue=
""
pe=
""
pe1=
""
pb=
""
isopen=
""
int
=
""
>>
""
";
var schema_equd =
""
"b.data.secID,
b.data.ticker,
b.data.secShortName,
b.data.exchangeCD,
b.data.tradeDate,
b.data.preClosePrice,
b.data.actPreClosePrice,
b.data.openPrice,
b.data.highestPrice,
b.data.lowestPrice,
b.data.closePrice,
b.data.turnoverVol,
b.data.turnoverValue,
b.data.dealAmount,
b.data.turnoverRate,
b.data.accumAdjFactor,
b.data.negMarketValue,
b.data.marketValue,
b.data.PE,
b.data.PE1,
b.data.PB,
b.data.isOpen
""
";
hiveConfigList = hiveConfigList :+ HiveConfig(
"finance"
,
"equd"
,
"hdfs://wdp.xxxxx.cn:8020/nifi/finance1/"
, schema_json_equd_serde, schema_equd);
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// 创建idxd数据结构
// 定义json结构
val schema_json_idxd_serde =
""
" retCode string,
retMsg string,
data array<struct< indexid:string,=
""
tradedate:date,=
""
ticker:string,=
""
porgfullname:string,=
""
secshortname:string,=
""
exchangecd:string,=
""
precloseindex:
double
,=
""
openindex:
double
,=
""
lowestindex:
double
,=
""
highestindex:
double
,=
""
closeindex:
double
,=
""
turnovervol:
double
,=
""
turnovervalue:
double
,=
""
chg:
double
,=
""
chgpct:
double
=
""
>>
""
";
var schema_idxd =
""
"b.data.indexID,
b.data.tradeDate,
b.data.ticker,
b.data.porgFullName,
b.data.secShortName,
b.data.exchangeCD,
b.data.preCloseIndex,
b.data.openIndex,
b.data.lowestIndex,
b.data.highestIndex,
b.data.closeIndex,
b.data.turnoverVol,
b.data.turnoverValue,
b.data.CHG,
b.data.CHGPct
""
";
hiveConfigList = hiveConfigList :+ HiveConfig(
"finance"
,
"idxd"
,
"hdfs://wdp.xxxxx.cn:8020/nifi/finance1/"
, schema_json_idxd_serde, schema_idxd);
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// 循环加载数据中
def loadDataToHive(args:HiveConfig){
val loadPath = args.hdfsPath + args.modelName;
val tb_json_serde =
"json_serde_"
+ args.modelName +
"_table"
;
val tb=
"tb_"
+ args.modelName;
val hiveContext =
new
org.apache.spark.sql.hive.HiveContext(sc)
if
(args.database !=
""
&& args.schema !=
""
) {
print(
"正在创建项目..."
+ args.modelName)
hiveContext.sql(
"CREATE DATABASE IF NOT EXISTS "
+ args.database);
print(
"正在构造扩展模型..."
);
hiveContext.sql(
"CREATE TABLE IF NOT EXISTS "
+ args.database +
"."
+ tb_json_serde +
"("
+ args.schema +
") row format serde 'org.apache.hive.hcatalog.data.JsonSerDe' LOCATION "
+
"'"
+ loadPath +
"/'"
);
println(
"CREATE TABLE IF NOT EXISTS "
+ args.database +
"."
+ tb +
" as select "
+ args.schema_tb +
" from "
+ args.database +
"."
+ tb_json_serde +
" LATERAL VIEW explode("
+ tb_json_serde +
".data) b AS data"
);
hiveContext.sql(
"CREATE TABLE IF NOT EXISTS "
+ args.database +
"."
+ tb +
" as select "
+ args.schema_tb +
" from "
+ args.database +
"."
+ tb_json_serde +
" LATERAL VIEW explode("
+ tb_json_serde +
".data) b AS data"
);
println(args.modelName +
" 扩展模型加载已完成!"
);
}
}
hiveConfigList.size;
hiveConfigList.foreach { x => loadDataToHive(x) };
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3. 第二种取法
由于data是json数据里的一个数组，所以上面的转换复杂了一点。下面这种方法是先把json里data数组取出来放到hdfs，然后直接用下面的语句放到hive：
用splitjson 来提取、分隔 data 数组
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CREATE EXTERNAL TABLE
if
not exists finance.awen_optd (
secid string,
tradedate date,
optid string,
ticker string,
secshortname string,
exchangecd string,
presettleprice
double
,
precloseprice
double
,
openprice
double
,
highestprice
double
,
lowestprice
double
,
closeprice
double
,
settlprice
double
,
turnovervol
double
,
turnovervalue
double
,
openint
int
)
ROW FORMAT SERDE
'org.apache.hive.hcatalog.data.JsonSerDe'
LOCATION
'hdfs://wdp.xxxx.cn:8020/nifi/finance2/optd/'
;
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本文转自疯吻IT博客园博客，原文链接：http://www.cnblogs.com/fengwenit/p/5631455.html，如需转载请自行联系原作者
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