具有自定义标头名称的Swagger / OpenAPI Bearer身份验证

时间：2019-06-03 12:04:47

标签： authentication swagger openapi swagger-editor

我正在使用OpenAPI定义（部分）现有API（Samanage），以协助进行一些集成工作。

我需要使用Bearer auth进行身份验证，但需要通过在Authorize以外的标头中发送令牌来进行身份验证。

服务器希望在名为X-Samanage-Authorization的标头中进行Bearer身份验证，如下例所示：

curl -H "X-Samanage-Authorization: Bearer <TokenGoesHere>" -H 'Accept: application/vnd.samanage.v2.1+json' -H 'Content-Type: application/json' -X GET https://api.samanage.com/incidents.json

我知道https://swagger.io/docs/specification/authentication/bearer-authentication/，但似乎无法完全帮助我。

此（OpenAPI 3）

components:
  securitySchemes:
    bearerAuth:
      type: http
      scheme: bearer
      bearerFormat: JWT
...
security:
- bearerAuth: []

得到一个名为默认（Authorization）的身份验证标头

curl -X GET "https://api.samanage.com/incidents/12341234.json" -H "accept: application/json" -H "Authorization: Bearer <TokenGoesHere>"

然后失败（401）。

我觉得我想要这个：

components:
  securitySchemes:
    bearerAuth:
      type: http
      name: X-Samanage-Authorization
      in: header
      scheme: bearer

但是，这在Swagger编辑器中的验证失败，因为我相信type的{​​{1}}不允许http组件（就像name类型一样）。老实说，我无法完全理解文档here。

我确实读过Specification Extensions，但是对于OpenAPI来说是完全陌生的，我找不到任何有关如何实际实现所需内容的示例。

任何见识都值得赞赏！

1 个答案:

答案 0 :(得分：0)

Dataset<Row> companyInfo_df = company_info_df .select("companyInfo.*" ) .withColumn("companyInfoEventTs", ( col("eventTs").divide(1000) ).cast(DataTypes.TimestampType)) .withWatermark("companyInfoEventTs", "60 seconds"); Dataset<Row> companyFin_df = comapany_fin_df .select("companyFin.*" ) .withColumn("eventTimeStamp", ( col("eventTs").divide(1000) ).cast(DataTypes.TimestampType)) .withWatermark("eventTimeStamp", "60 seconds") .groupBy( window(col("eventTimeStamp").cast(DataTypes.TimestampType), "30 seconds", "20 seconds", "10 seconds") ,col("company_id"),col("year"),col("quarter") ) .agg( min("revenue").alias("min_revenue"), max("revenue").alias("max_revenue") , avg("revenue").alias("mean_revenue"), first("eventTimeStamp").alias("companyFinEventTs") ) .select("company_id","year", "quarter", "companyFinEventTs", "window.start","window.end","min_revenue", "max_revenue","mean_revenue"); Dataset<Row> companyFinWithWatermark = companyFin_df.withWatermark("companyFinEventTs", "2 minutes"); Dataset<Row> companyInfoWithWatermark = companyInfo_df.withWatermark("companyInfoEventTs", "3 minutes"); Column joinExpr = expr(" company_id = companyid AND companyFinEventTs >= companyInfoEventTs AND companyFinEventTs <= companyInfoEventTs + interval 1 minutes "); Dataset<Row> companyDfAfterJoin2 = companyFinWithWatermark.join(companyInfoWithWatermark, joinExpr //,"leftOuter" ) .withColumn("last_update_timestamp", current_timestamp()) .withColumn( "avg_revenue", col("mean_revenue")) Dataset<Row> companyDfAfterJoin = companyDfAfterJoin2 //.withWatermark("companyFinEventTs", "60 seconds") .select("company_id","company_name","year","quarter", "avg_revenue" ,"last_update_timestamp" , "companyFinEventTs"); System.out.println(" companyDfAfterJoin *******************************"); companyDfAfterJoin .writeStream() .format("console") .outputMode("append") .option("truncate", false) .trigger(Trigger.ProcessingTime("10 seconds")) .start(); 用于由RFC 7235和IANA HTTP Authentication Scheme Registry定义的HTTP认证。根据定义，HTTP身份验证使用companyDfAfterJoin ******************************* Exception in thread "main" org.apache.spark.sql.AnalysisException: Append output mode not supported when there are streaming aggregations on streaming DataFrames/DataSets without watermark;; Project [company_id#102, company_name#64, year#103, quarter#104, avg_revenue#216, last_update_timestamp#200, companyFinEventTs#137-T120000ms] +- Project [company_id#102, company_name#64, year#103, quarter#104, avg_revenue#216, last_update_timestamp#200, companyFinEventTs#137-T120000ms] +- Project [company_id#102, year#103, quarter#104, companyFinEventTs#137-T120000ms, start#147, end#148, min_revenue#131, max_revenue#133, mean_revenue#135, company_name#64, registeredYear#66, headQuarteredCity#67, companyid#74, companyInfoEventTs#81-T180000ms, last_update_timestamp#200, mean_revenue#135 AS avg_revenue#216] +- Project [company_id#102, year#103, quarter#104, companyFinEventTs#137-T120000ms, start#147, end#148, min_revenue#131, max_revenue#133, mean_revenue#135, company_name#64, registeredYear#66, headQuarteredCity#67, companyid#74, companyInfoEventTs#81-T180000ms, current_timestamp() AS last_update_timestamp#200] +- Join Inner, (((company_id#102 = companyid#74) && (companyFinEventTs#137-T120000ms >= companyInfoEventTs#81-T180000ms)) && (companyFinEventTs#137-T120000ms <= cast(companyInfoEventTs#81-T180000ms + interval 1 minutes as timestamp))) :- EventTimeWatermark companyFinEventTs#137: timestamp, interval 2 minutes : +- Project [company_id#102, year#103, quarter#104, companyFinEventTs#137, window#124.start AS start#147, window#124.end AS end#148, min_revenue#131, max_revenue#133, mean_revenue#135] : +- Aggregate [window#138, company_id#102, year#103, quarter#104], [window#138 AS window#124, company_id#102, year#103, quarter#104, min(revenue#105) AS min_revenue#131, max(revenue#105) AS max_revenue#133, avg(cast(revenue#105 as bigint)) AS mean_revenue#135, first(eventTimeStamp#112-T60000ms, false) AS companyFinEventTs#137] : +- Filter ((cast(eventTimeStamp#112-T60000ms as timestamp) >= window#138.start) && (cast(eventTimeStamp#112-T60000ms as timestamp) < window#138.end)) : +- Expand [ArrayBuffer(named_struct(start, precisetimestampconversion(((((CASE WHEN (cast(CEIL((cast((precisetimestampconversion(cast(eventTimeStamp#112-T60000ms as timestamp), TimestampType, LongType) - 10000000) as double) / cast(20000000 as double))) as double) = (cast((precisetimestampconversion(cast(eventTimeStamp#112-T60000ms as timestamp), TimestampType, LongType) - 10000000) as double) / cast(20000000 as double))) THEN (CEIL((cast((precisetimestampconversion(cast(eventTimeStamp#112-T60000ms as timestamp), TimestampType, LongType) - 10000000) as double) / cast(20000000 as double))) + cast(1 as bigint)) ELSE CEIL((cast((precisetimestampconversion(cast(eventTimeStamp#112-T60000ms as timestamp), TimestampType, LongType) - 10000000) as double) / cast(20000000 as double))) END + cast(0 as bigint)) - cast(2 as bigint)) * 20000000) + 10000000), LongType, TimestampType), end, precisetimestampconversion((((((CASE WHEN (cast(CEIL((cast((precisetimestampconversion(cast(eventTimeStamp#112-T60000ms as timestamp), TimestampType, LongType) - 10000000) as double) / cast(20000000 as double))) as double) = (cast((precisetimestampconversion(cast(eventTimeStamp#112-T60000ms as timestamp), TimestampType, LongType) - 10000000) as double) / cast(20000000 as double))) THEN (CEIL((cast((precisetimestampconversion(cast(eventTimeStamp#112-T60000ms as timestamp), TimestampType, LongType) - 10000000) as double) / cast(20000000 as double))) + cast(1 as bigint)) ELSE CEIL((cast((precisetimestampconversion(cast(eventTimeStamp#112-T60000ms as timestamp), TimestampType, LongType) - 10000000) as double) / cast(20000000 as double))) END + cast(0 as bigint)) - cast(2 as bigint)) * 20000000) + 10000000) + 30000000), LongType, TimestampType)), company_id#102, year#103, quarter#104, revenue#105, eventTimeStamp#112-T60000ms), ArrayBuffer(named_struct(start, precisetimestampconversion(((((CASE WHEN (cast(CEIL((cast((precisetimestampconversion(cast(eventTimeStamp#112-T60000ms as timestamp), TimestampType, LongType) - 10000000) as double) / cast(20000000 as double))) as double) = (cast((precisetimestampconversion(cast(eventTimeStamp#112-T60000ms as timestamp), TimestampType, LongType) - 10000000) as double) / cast(20000000 as double))) THEN (CEIL((cast((precisetimestampconversion(cast(eventTimeStamp#112-T60000ms as timestamp), TimestampType, LongType) - 10000000) as double) / cast(20000000 as double))) + cast(1 as bigint)) ELSE CEIL((cast((precisetimestampconversion(cast(eventTimeStamp#112-T60000ms as timestamp), TimestampType, LongType) - 10000000) as double) / cast(20000000 as double))) END + cast(1 as bigint)) - cast(2 as bigint)) * 20000000) + 10000000), LongType, TimestampType), end, precisetimestampconversion((((((CASE WHEN (cast(CEIL((cast((precisetimestampconversion(cast(eventTimeStamp#112-T60000ms as timestamp), TimestampType, LongType) - 10000000) as double) / cast(20000000 as double))) as double) = (cast((precisetimestampconversion(cast(eventTimeStamp#112-T60000ms as timestamp), TimestampType, LongType) - 10000000) as double) / cast(20000000 as double))) THEN (CEIL((cast((precisetimestampconversion(cast(eventTimeStamp#112-T60000ms as timestamp), TimestampType, LongType) - 10000000) as double) / cast(20000000 as double))) + cast(1 as bigint)) ELSE CEIL((cast((precisetimestampconversion(cast(eventTimeStamp#112-T60000ms as timestamp), TimestampType, LongType) - 10000000) as double) / cast(20000000 as double))) END + cast(1 as bigint)) - cast(2 as bigint)) * 20000000) + 10000000) + 30000000), LongType, TimestampType)), company_id#102, year#103, quarter#104, revenue#105, eventTimeStamp#112-T60000ms)], [window#138, company_id#102, year#103, quarter#104, revenue#105, eventTimeStamp#112-T60000ms] : +- EventTimeWatermark eventTimeStamp#112: timestamp, interval 1 minutes : +- Project [company_id#102, year#103, quarter#104, revenue#105, eventTimeStamp#112] : +- Project [company_id#102, year#103, quarter#104, revenue#105, eventTs#106L, cast((cast(eventTs#106L as double) / cast(1000 as double)) as timestamp) AS eventTimeStamp#112] : +- Project [companyFin#100.company_id AS company_id#102, companyFin#100.year AS year#103, companyFin#100.quarter AS quarter#104, companyFin#100.revenue AS revenue#105, companyFin#100.eventTs AS eventTs#106L] : +- Project [jsontostructs(StructField(company_id,IntegerType,true), StructField(year,IntegerType,true), StructField(quarter,StringType,true), StructField(revenue,IntegerType,true), StructField(eventTs,LongType,true), cast(value#29 as string), Some(America/New_York)) AS companyFin#100] : +- StreamingRelationV2 org.apache.spark.sql.kafka010.KafkaSourceProvider@5f935d49, kafka, Map(includeTimestamp -> true, key.deserializer -> org.apache.kafka.common.serialization.StringDeserializer, retries -> 1, subscribe -> inbound_company_financials, group.id -> ProcessingGroup, enable.auto.commit -> false, failOnDataLoss -> false, value.deserializer -> com.spg.ca.prescore.serde.KafkaJsonCompanyFinRecordSerDe, kafka.bootstrap.servers -> localhost:9092, startingOffsets -> latest, linger.ms -> 5), [key#28, value#29, topic#30, partition#31, offset#32L, timestamp#33, timestampType#34], StreamingRelation DataSource(org.apache.spark.sql.SparkSession@76af34b5,kafka,List(),None,List(),None,Map(includeTimestamp -> true, key.deserializer -> org.apache.kafka.common.serialization.StringDeserializer, retries -> 1, subscribe -> inbound_company_financials, group.id -> ProcessingGroup, enable.auto.commit -> false, failOnDataLoss -> false, value.deserializer -> com.spg.ca.prescore.serde.KafkaJsonCompanyFinRecordSerDe, kafka.bootstrap.servers -> localhost:9092, startingOffsets -> latest, linger.ms -> 5),None), kafka, [key#21, value#22, topic#23, partition#24, offset#25L, timestamp#26, timestampType#27] +- EventTimeWatermark companyInfoEventTs#81: timestamp, interval 3 minutes +- Project [company_name#64, registeredYear#66, headQuarteredCity#67, companyid#74, companyInfoEventTs#81-T60000ms] +- Project [company_name#64, company_id#65, registeredYear#66, headQuarteredCity#67, companyid#74, companyInfoEventTs#81-T60000ms] +- EventTimeWatermark companyInfoEventTs#81: timestamp, interval 1 minutes +- Project [company_name#64, company_id#65, registeredYear#66, headQuarteredCity#67, eventTs#68L, companyid#74, cast((cast(eventTs#68L as double) / cast(1000 as double)) as timestamp) AS companyInfoEventTs#81] +- Project [company_name#64, company_id#65, registeredYear#66, headQuarteredCity#67, eventTs#68L, cast(company_id#65 as int) AS companyid#74] +- Project [companyInfo#62.company_name AS company_name#64, companyInfo#62.company_id AS company_id#65, companyInfo#62.registeredYear AS registeredYear#66, companyInfo#62.headQuarteredCity AS headQuarteredCity#67, companyInfo#62.eventTs AS eventTs#68L] +- Project [jsontostructs(StructField(company_name,StringType,true), StructField(company_id,IntegerType,true), StructField(registeredYear,IntegerType,true), StructField(headQuarteredCity,StringType,true), StructField(eventTs,LongType,true), cast(value#42 as string), Some(America/New_York)) AS companyInfo#62] +- Project [cast(value#8 as string) AS value#42, cast(topic#9 as string) AS topic#43, cast(partition#10 as int) AS partition#44, cast(offset#11L as bigint) AS offset#45L, cast(timestamp#12 as timestamp) AS timestamp#46] +- StreamingRelationV2 org.apache.spark.sql.kafka010.KafkaSourceProvider@3313463c, kafka, Map(includeTimestamp -> true, key.deserializer -> org.apache.kafka.common.serialization.StringDeserializer, retries -> 1, subscribe -> inbound_company_info, group.id -> ProcessingGroup, enable.auto.commit -> false, failOnDataLoss -> false, value.deserializer -> com.spg.ca.prescore.serde.KafkaJsonCompanyInfoRecordSerDe, kafka.bootstrap.servers -> localhost:9092, startingOffsets -> latest, linger.ms -> 5), [key#7, value#8, topic#9, partition#10, offset#11L, timestamp#12, timestampType#13], StreamingRelation DataSource(org.apache.spark.sql.SparkSession@76af34b5,kafka,List(),None,List(),None,Map(includeTimestamp -> true, key.deserializer -> org.apache.kafka.common.serialization.StringDeserializer, retries -> 1, subscribe -> inbound_company_info, group.id -> ProcessingGroup, enable.auto.commit -> false, failOnDataLoss -> false, value.deserializer -> com.spg.ca.prescore.serde.KafkaJsonCompanyInfoRecordSerDe, kafka.bootstrap.servers -> localhost:9092, startingOffsets -> latest, linger.ms -> 5),None), kafka, [key#0, value#1, topic#2, partition#3, offset#4L, timestamp#5, timestampType#6] at org.apache.spark.sql.catalyst.analysis.UnsupportedOperationChecker$.org$apache$spark$sql$catalyst$analysis$UnsupportedOperationChecker$$throwError(UnsupportedOperationChecker.scala:389) at org.apache.spark.sql.catalyst.analysis.UnsupportedOperationChecker$.checkForStreaming(UnsupportedOperationChecker.scala:111) at org.apache.spark.sql.streaming.StreamingQueryManager.createQuery(StreamingQueryManager.scala:250) at org.apache.spark.sql.streaming.StreamingQueryManager.startQuery(StreamingQueryManager.scala:316) at org.apache.spark.sql.streaming.DataStreamWriter.start(DataStreamWriter.scala:325) at com.spgmi.ca.prescore.utils.ConfigUtils.displayOnConsole(ConfigUtils.java:84) 标头。

要使用自定义标头名称，您需要将其定义为API key（type: http）：

Authorization

请注意，由于这是非标准的Bearer方案，因此客户端需要手动在令牌值中添加“ Bearer”前缀。例如，当您在Swagger UI中单击“授权”时，您需要输入“ Bearer TOKEN”，而不仅仅是“ TOKEN”。

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