From 127cf5207a7065e4612eebde6ff377142ad5a9ba Mon Sep 17 00:00:00 2001
From: okanmercan <okanmercan16@gmail.com>
Date: Thu, 20 Jul 2023 14:41:34 +0300
Subject: [PATCH] :recycle: refactor(ExecutionService): Rename jobErrorLogs as
 mappingErrorLogs to prevent confusion.

---
 .../tofhir/server/service/ExecutionService.scala | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/tofhir-server/src/main/scala/io/tofhir/server/service/ExecutionService.scala b/tofhir-server/src/main/scala/io/tofhir/server/service/ExecutionService.scala
index 63796f2e..21ea9532 100644
--- a/tofhir-server/src/main/scala/io/tofhir/server/service/ExecutionService.scala
+++ b/tofhir-server/src/main/scala/io/tofhir/server/service/ExecutionService.scala
@@ -147,27 +147,27 @@ class ExecutionService(jobRepository: IJobRepository, mappingRepository: IMappin
         Seq.empty
       }
       else {
-        // Get job run logs for the given execution. ProjectId field is not null for selecting jobRunsLogs, filter out job error logs.
+        // Get job run logs for the given execution. ProjectId field is not null for selecting jobRunsLogs, filter out mapping error logs.
         val jobRunLogs = dataFrame.filter(s"executionId = '$executionId' and projectId is not null")
 
         // Handle the case where the job has not been run yet, which makes the data frame empty
         if (jobRunLogs.isEmpty) {
           Seq.empty
         } else {
-          // Get error logs for the given execution and select needed columns. ProjectId field is null for selecting job error logs, filter out jobRunsLogs.
-          val jobErrorLogs = dataFrame.filter(s"executionId = '$executionId' and projectId is null").select(List("errorCode", "errorDesc", "message", "mappingUrl").map(col):_*)
+          // Get error logs for the given execution and select needed columns. ProjectId field is null for selecting mapping error logs, filter out jobRunsLogs.
+          val mappingErrorLogs = dataFrame.filter(s"executionId = '$executionId' and projectId is null").select(List("errorCode", "errorDesc", "message", "mappingUrl").map(col):_*)
 
-          // Group job error logs by mapping url
-          val jobErrorLogsGroupedByMappingUrl = jobErrorLogs.groupByKey(row => row.get(row.fieldIndex("mappingUrl")).toString)(Encoders.STRING)
+          // Group mapping error logs by mapping url
+          val jobErrorLogsGroupedByMappingUrl = mappingErrorLogs.groupByKey(row => row.get(row.fieldIndex("mappingUrl")).toString)(Encoders.STRING)
 
-          // Collect job run logs for matching with mappingUrl field of job error logs
+          // Collect job run logs for matching with mappingUrl field of mapping error logs
           var jobRunLogsData = jobRunLogs.collect()
 
-          // Add error details to job run logs if any error occurred while executing the job.
+          // Add mapping error details to job run logs if any error occurred while executing the job.
           val jobRunLogsWithErrorDetails = jobErrorLogsGroupedByMappingUrl.mapGroups((key, values) => {
             // Find the related job run log to given mapping url
             val jobRunLog = jobRunLogsData.filter(row => row.getAs[String]("mappingUrl") == key)
-            // Append error logs to the related job run log
+            // Append mapping error logs to the related job run log
             Row.fromSeq(Row.unapplySeq(jobRunLog.head).get :+ values.toSeq)
           })(
               // Define a new schema for the resulting rows and create an encoder for it. We will add a "error_logs" column to job run logs that contains related error logs.