你当前正在访问 Microsoft Azure Global Edition 技术文档网站。 如果需要访问由世纪互联运营的 Microsoft Azure 中国技术文档网站，请访问 https://docs.azure.cn。

查询通话日志

概览和访问权限

在为通信服务日志使用 Log Analytics 之前，必须首先按照在诊断设置中启用日志记录中所述的步骤进行操作。 启用日志和 Log Analytics 工作区后，可以访问许多有用的 默认查询包，这些默认查询包 可帮助你快速可视化和了解日志中可用的数据，如下所述。 通过 Log Analytics，还可以通过 Azure Monitor 工作簿访问更多通信服务见解、创建自己的查询和工作簿的功能、 Log Analytics API 概述 到任何查询。

Access

可以通过以下方式访问查询：转到你的通信服务资源页，然后在左侧导航栏的“监视”部分中单击“日志”：

在此处，你会看到一个模式屏幕，其中包含可用于通信服务的所有 默认查询包 ，以及可用于导航到左侧的查询包列表。

如果关闭模式屏幕，仍可以导航到各种查询包，根据诊断设置中启用的日志和指标的架构，直接访问表形式的数据。 此处，可以使用 KQL (Kusto) 从数据创建自己的查询。 有关使用、编辑和创建查询的详细信息，请参阅：Log Analytics 查询

适用于呼叫摘要和呼叫诊断日志的默认查询包

以下是默认查询包中每个查询的说明，适用于呼叫摘要和呼叫诊断日志，其中包括每个可用查询的代码示例和示例输出：

呼叫概览查询

每个呼叫的参与者数量

// Count number of calls and participants,
// and print average participants per call
ACSCallSummary
| distinct CorrelationId, ParticipantId, EndpointId
| summarize num_participants=count(), num_calls=dcount(CorrelationId)
| extend avg_participants = todecimal(num_participants) / todecimal(num_calls)

示例输出：

每个群组呼叫的参与者数量

// Count number of participants per group call
ACSCallSummary
| where CallType == 'Group'
| distinct CorrelationId, ParticipantId
| summarize num_participants=count() by CorrelationId
| summarize participant_counts=count() by num_participants
| order by num_participants asc 
| render columnchart with  (xcolumn = num_participants, title="Number of participants per group call")

示例输出：

呼叫类型的比例

// Ratio of call types
ACSCallSummary
| summarize call_types=dcount(CorrelationId) by CallType
| render piechart title="Call Type Ratio"

示例输出：

呼叫持续时间分布

// Call duration histogram
ACSCallSummary
| distinct CorrelationId, CallDuration
|summarize duration_counts=count() by CallDuration
| order by CallDuration asc
| render columnchart with (xcolumn = CallDuration, title="Call duration histogram")

示例输出：

呼叫持续时间百分位数

// Call duration percentiles
ACSCallSummary
| distinct CorrelationId, CallDuration
| summarize avg(CallDuration), percentiles(CallDuration, 50, 90, 99)

示例输出：

终结点信息查询

每个呼叫的终结点数量

// Count number of calls and endpoints,
// and print average endpoints per call
ACSCallSummary
| distinct CorrelationId, EndpointId
| summarize num_endpoints=count(), num_calls=dcount(CorrelationId)
| extend avg_endpoints = todecimal(num_endpoints) / todecimal(num_calls)

示例输出：

SDK 版本的比例

// Ratio of SDK Versions
ACSCallSummary
| distinct CorrelationId, ParticipantId, EndpointId, SdkVersion
| summarize sdk_counts=count() by SdkVersion
| order by SdkVersion asc
| render piechart title="SDK Version Ratio"

示例输出：

OS 版本的比例（简化的 OS 名称）

// Ratio of OS Versions (simplified OS name)
ACSCallSummary
| distinct CorrelationId, ParticipantId, EndpointId, OsVersion
| extend simple_os = case(  indexof(OsVersion, "Android") != -1, tostring(split(OsVersion, ";")[0]),
                            indexof(OsVersion, "Darwin") != -1, tostring(split(OsVersion, ":")[0]),
                            indexof(OsVersion, "Windows") != -1, tostring(split(OsVersion, ".")[0]),
                            OsVersion
                        )
| summarize os_counts=count() by simple_os
| order by simple_os asc
| render piechart title="OS Version Ratio"

示例输出：

媒体流查询

每个呼叫的流数量

// Count number of calls and streams,
// and print average streams per call
ACSCallDiagnostics
| summarize num_streams=count(), num_calls=dcount(CorrelationId)
| extend avg_streams = todecimal(num_streams) / todecimal(num_calls)

示例输出：

每个呼叫的流数量直方图

// Distribution of streams per call
ACSCallDiagnostics
| summarize streams_per_call=count() by CorrelationId
| summarize stream_counts=count() by streams_per_call
| order by streams_per_call asc
| render columnchart title="Streams per call histogram"

媒体类型的比例

// Ratio of media types by call
ACSCallDiagnostics
| summarize media_types=count() by MediaType
| render piechart title="Media Type Ratio"

质量指标查询

平均遥测值

// Average telemetry values over all streams
ACSCallDiagnostics
| summarize Avg_JitterAvg=avg(JitterAvg),
            Avg_JitterMax=avg(JitterMax),
            Avg_RoundTripTimeAvg=avg(RoundTripTimeAvg),
            Avg_RoundTripTimeMax=avg(RoundTripTimeMax),
            Avg_PacketLossRateAvg=avg(PacketLossRateAvg),
            Avg_PacketLossRateMax=avg(PacketLossRateMax)

JitterAvg 直方图

// Jitter Average Histogram
ACSCallDiagnostics
| where isnotnull(JitterAvg)
| summarize JitterAvg_counts=count() by JitterAvg
| order by JitterAvg asc
| render columnchart with (xcolumn = JitterAvg, title="JitterAvg histogram")

JitterMax 直方图

// Jitter Max Histogram
ACSCallDiagnostics
| where isnotnull(JitterMax)
|summarize JitterMax_counts=count() by JitterMax
| order by JitterMax asc
| render columnchart with (xcolumn = JitterMax, title="JitterMax histogram")

PacketLossRateAvg 直方图

// PacketLossRate Average Histogram
ACSCallDiagnostics
| where isnotnull(PacketLossRateAvg)
|summarize PacketLossRateAvg_counts=count() by bin(PacketLossRateAvg, 0.01)
| order by PacketLossRateAvg asc
| render columnchart with (xcolumn = PacketLossRateAvg, title="PacketLossRateAvg histogram")

PacketLossRateMax 直方图

// PacketLossRate Max Histogram
ACSCallDiagnostics
| where isnotnull(PacketLossRateMax)
|summarize PacketLossRateMax_counts=count() by bin(PacketLossRateMax, 0.01)
| order by PacketLossRateMax asc
| render columnchart with (xcolumn = PacketLossRateMax, title="PacketLossRateMax histogram")

RoundTripTimeAvg 直方图

// RoundTripTime Average Histogram
ACSCallDiagnostics
| where isnotnull(RoundTripTimeAvg)
|summarize RoundTripTimeAvg_counts=count() by RoundTripTimeAvg
| order by RoundTripTimeAvg asc
| render columnchart with (xcolumn = RoundTripTimeAvg, title="RoundTripTimeAvg histogram")

RoundTripTimeMax 直方图

// RoundTripTime Max Histogram
ACSCallDiagnostics
| where isnotnull(RoundTripTimeMax)
|summarize RoundTripTimeMax_counts=count() by RoundTripTimeMax
| order by RoundTripTimeMax asc
| render columnchart with (xcolumn = RoundTripTimeMax, title="RoundTripTimeMax histogram")

抖动质量较差

// Get proportion of calls with poor quality jitter
// (defined as jitter being higher than 30ms)
ACSCallDiagnostics
| extend JitterQuality = iff(JitterAvg > 30, "Poor", "Good")
| summarize count() by JitterQuality
| render piechart title="Jitter Quality"

PacketLossRate 质量

// Get proportion of calls with poor quality packet loss
// rate (defined as packet loss being higher than 10%)
ACSCallDiagnostics
| extend PacketLossRateQuality = iff(PacketLossRateAvg > 0.1, "Poor", "Good")
| summarize count() by PacketLossRateQuality
| render piechart title="Packet Loss Rate Quality"

RoundTripTime 质量

// Get proportion of calls with poor quality packet loss
// rate (defined as packet loss being higher than 10%)
ACSCallDiagnostics
| extend PacketLossRateQuality = iff(PacketLossRateAvg > 0.1, "Poor", "Good")
| summarize count() by PacketLossRateQuality
| render piechart title="Packet Loss Rate Quality"

可参数化查询

上周的每日呼叫数量

// Histogram of daily calls over the last week
ACSCallSummary
| where CallStartTime > now() - 7d
| distinct CorrelationId, CallStartTime
| extend hour  = floor(CallStartTime, 1d)
| summarize event_count=count() by day
| sort by day asc
| render columnchart title="Number of calls in last week"

前一天每小时的呼叫数量

// Histogram of calls per hour in the last day
ACSCallSummary
| where CallStartTime > now() - 1d
| distinct CorrelationId, CallStartTime
| extend hour = floor(CallStartTime, 1h)
| summarize event_count=count() by hour
| sort by hour asc
| render columnchart title="Number of calls per hour in last day"