我正在尝试使用此摄取斑点加载德鲁伊的TSV:
最近更新的规格:
{
"type" : "index",
"spec" : {
"ioConfig" : {
"type" : "index",
"inputSpec" : {
"type": "local",
"baseDir": "quickstart",
"filter": "test_data.json"
}
},
"dataSchema" : {
"dataSource" : "local",
"granularitySpec" : {
"type" : "uniform",
"segmentGranularity" : "hour",
"queryGranularity" : "none",
"intervals" : ["2016-07-18/2016-07-22"]
},
"parser" : {
"type" : "string",
"parseSpec" : {
"format" : "json",
"dimensionsSpec" : {
"dimensions" : ["name", "email", "age"]
},
"timestampSpec" : {
"format" : "yyyy-MM-dd HH:mm:ss",
"column" : "date"
}
}
},
"metricsSpec" : [
{
"name" : "count",
"type" : "count"
},
{
"type" : "doubleSum",
"name" : "age",
"fieldName" : "age"
}
]
}
}
}
如果我的架构如下所示:
Schema: name email age
实际数据集如下所示:
name email age Bob Jones 23 Billy Jones 45
这是如何在TSV的上述数据集中格式化列^^?像name email age
应该是第一个(列),然后是实际数据。我很困惑德鲁伊如何知道如何将列映射到TSV格式的实际数据集。
答案 0 :(得分:3)
TSV代表制表符分隔格式,因此它看起来与csv相同,但您将使用制表符而不是逗号,例如
Name<TAB>Age<TAB>Address
Paul<TAB>23<TAB>1115 W Franklin
Bessy the Cow<TAB>5<TAB>Big Farm Way
Zeke<TAB>45<TAB>W Main St
您将使用frist line作为标题来定义列名称 - 因此您可以使用&#34; name&#34;,&#34; age&#34;或者&#34;电子邮件&#34;在spec文件中的维度
对于gmt和utc,它们基本相同
格林威治标准时间与格林威治标准时间没有时间差异 协调世界时
第一个是时区,另一个是时间标准
不要忘记在你的tsv文件中包含一些具有时间值的列!!所以,例如如果你有tsv文件看起来像:
"name" "position" "office" "age" "start_date" "salary"
"Airi Satou" "Accountant" "Tokyo" "33" "2016-07-16T19:20:30+01:00" "162700"
"Angelica Ramos" "Chief Executive Officer (CEO)" "London" "47" "2016-07-16T19:20:30+01:00" "1200000"
您的spec文件应如下所示:
{
"spec" : {
"ioConfig" : {
"inputSpec" : {
"type": "local",
"baseDir": "path_to_folder",
"filter": "name_of_the_file(s)"
}
},
"dataSchema" : {
"dataSource" : "local",
"granularitySpec" : {
"type" : "uniform",
"segmentGranularity" : "hour",
"queryGranularity" : "none",
"intervals" : ["2016-07-01/2016-07-28"]
},
"parser" : {
"type" : "string",
"parseSpec" : {
"format" : "tsv",
"dimensionsSpec" : {
"dimensions" : [
"position",
"age",
"office"
]
},
"timestampSpec" : {
"format" : "auto",
"column" : "start_date"
}
}
},
"metricsSpec" : [
{
"name" : "count",
"type" : "count"
},
{
"name" : "sum_sallary",
"type" : "longSum",
"fieldName" : "salary"
}
]
}
}
}