Error In Internal Percentage Request Resulted
be used to manage Oracle HTTP Server. You can use the All Metrics page for an HTTP Server target to view the metrics that have been collected for that target by the Oracle Management Agent. 15.1 Host Metrics Metrics for the host on which the HTTP Server is running. 15.1.1 Name This is the host name. 15.2 mod_oc4j Destination Metrics The metrics in this category provide details about the successful and failed requests routed by mod_oc4j to a particular OC4J http://www.symantec.com/connect/forums/sepm-server-got-internal-error-request-resulted-internal-error Instance. The metrics table shows details such as the OC4J instances to which the requests were routed, the total number of successful and failed requests routed by mod_oc4j to a particular OC4J instance. The following table lists the metrics and their descriptions. Note: For target versions 9.0.4.x and 10.1.2.x, the collection frequency for each metric is every http://web.deu.edu.tr/doc/oracle/B16240_01/em.102/b25987/oracle_apache.htm 30 minutes. Table 15-1 mod_oc4j Destination Metrics Metric Description Failover.count, ops Total number of failovers for this destination Percentage of Requests that Were Failures See Section 15.2.1, "Percentage of Requests that Were Failures" Percentage of Requests that Were Session Requests Percentage of total number of requests routed by mod_oc4j to this particular OC4J instance that were session requests, during the last collection interval Requests Per Second Routed to Destination Number of requests routed per second by mod_oc4j to this particular OC4J instance Total Failed Requests to Destination Total number of failed requests routed by mod_oc4j to this particular OC4J instance Total Successful Requests to Destination Total number of successful requests routed by mod_oc4j to this particular OC4J instance 15.2.1 Percentage of Requests that Were Failures The percentage of the total number of requests routed by mod_oc4j to this particular OC4J instance that were failed requests. Metric Summary The following table shows how often the metric's value is collected and compared against the default thresholds. The 'Consecutive Number of
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view the current status of the services listed below. For additional information on these services, please visit cloud.google.com. Google BigQuery Incident #18018 Streaming API issues with BigQuery Incident began at 2016-07-25 17:03 and ended at 2016-07-25 18:14 (all times are US/Pacific). Date Time Description Jul 27, 2016 16:06 SUMMARY: On Monday 25 July 2016, the Google BigQuery Streaming API experienced elevated error rates for a duration of 71 minutes. We apologize if your service or application was affected by this and we are taking immediate steps to improve the platform’s performance and availability. DETAILED DESCRIPTION OF IMPACT: On Monday 25 July 2016 between 17:03 and 18:14 PDT, the BigQuery Streaming API returned HTTP 500 or 503 errors for 35% of streaming insert requests, with a peak error rate of 49% at 17:40. Customers who retried on error were able to mitigate the impact. Calls to the BigQuery jobs API showed an error rate of 3% during the incident but could generally be executed reliably with normal retry behaviour. Other BigQuery API calls were not affected. ROOT CAUSE: An internal Google service sent an unexpectedly high amount of traffic to the BigQuery Streaming API service. The internal service used a different entry point that was not subject to quota limits. Google's internal load balancers drop requests that exceed the capacity limits of a service. In this case, the capacity limit for the Streaming API service had been configured higher than its true capacity. As a result, the internal Google service was able to send too many requests to the Streaming API, causing it to fail for a percentage of responses. The Streaming API service sends requests to BigQuery's Metadata service in order to handle incoming Streaming requests. This elevated volume of requests exceeded the capacity of the Metadata service which resulted in errors for BigQuery jobs API calls. REMEDIATION AND PREVENTION: The incident started at 17:03. Our monitoring detected the issue at 17:20 as error rates started to increase. Our engineers blocked traffic from the internal Google client causing the overload shortly thereafter which immediately started to mitigate the impact of the incident. Error rates dropped to normal by 18:14. In order to prevent a r