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# 擷取資料目錄中 AWS Glue 的資料以進行 Amazon Chime SDK 呼叫分析
<a name="ca-data-model-queries"></a>

使用這些範例查詢來擷取和整理 Amazon Chime SDK 呼叫分析 Glue 資料目錄中的資料。

**注意**  
如需有關連線至 Amazon Athena 和查詢 Glue 資料目錄的資訊，請參閱[使用 ODBC 連線至 Amazon Athena](https://docs.aws.amazon.com/athena/latest/ug/connect-with-odbc.html)。

請根據需要展開每個區段。

## 從 call\$1analytics\$1metadata 資料表中的中繼資料 (STRING 資料類型） 擷取值
<a name="qry-insights-metadata"></a>

`call_analytics_metadata` 具有 JSON 字串格式`metadata`的欄位。使用 Athena 中的 [json\$1extract\$1scalar 函數](https://docs.aws.amazon.com/athena/latest/ug/extracting-data-from-JSON.html)來查詢此字串中的元素。

```
SELECT
    json_extract_scalar(metadata,'$.voiceConnectorId') AS "VoiceConnector ID",
    json_extract_scalar(metadata,'$.fromNumber') AS "From Number",
    json_extract_scalar(metadata,'$.toNumber') AS "To Number",
    json_extract_scalar(metadata,'$.callId') AS "Call ID",
    json_extract_scalar(metadata,'$.direction') AS Direction,
    json_extract_scalar(metadata,'$.transactionId') AS "Transaction ID"
FROM 
    "GlueDatabaseName"."call_analytics_metadata"
```

## 在 call\$1analytics\$1metadata 資料表中查詢 SIPRECMetadata 更新
<a name="qry-insights-siprec-metadata"></a>

`call_analytics_metadata` 欄位具有 JSON 字串格式的中繼資料欄位。 `metadata` 具有另一個名為 的巢狀物件`oneTimeMetadata`，此物件包含原始 XML 和轉換 JSON 格式的 SIPRec 中繼資料。使用 Athena 中的 `json_extract_scalar`函數來查詢此字串中的元素。

```
SELECT
    json_extract_scalar(metadata,'$.voiceConnectorId') AS "VoiceConnector ID",
    json_extract_scalar(metadata,'$.fromNumber') AS "From Number",
    json_extract_scalar(metadata,'$.toNumber') AS "To Number",
    json_extract_scalar(metadata,'$.callId') AS "Call ID",
    json_extract_scalar(metadata,'$.direction') AS Direction,
    json_extract_scalar(metadata,'$.transactionId') AS "Transaction ID",
    json_extract_scalar(json_extract_scalar(metadata,'$.oneTimeMetadata'),'$.siprecMetadata') AS "siprec Metadata XML",
    json_extract_scalar(json_extract_scalar(metadata,'$.oneTimeMetadata'),'$.siprecMetadataJson') AS "Siprec Metadata JSON",
    json_extract_scalar(json_extract_scalar(metadata,'$.oneTimeMetadata'),'$.inviteHeaders') AS "Invite Headers"
FROM 
    "GlueDatabaseName"."call_analytics_metadata"
WHERE 
    callevent-type = "update";
```

## 從 call\$1analytics\$1recording\$1metadata 資料表中的中繼資料 (STRING 資料類型） 擷取值
<a name="qry-recording-metadata"></a>

`call_analytics_recording_metadata` 具有 JSON 字串格式的中繼資料欄位。使用 Athena 中的 [json\$1extract\$1scalar 函數](https://docs.aws.amazon.com/athena/latest/ug/extracting-data-from-JSON.html)來查詢此字串中的元素。

```
SELECT
    json_extract_scalar(metadata,'$.voiceConnectorId') AS "VoiceConnector ID",
    json_extract_scalar(metadata,'$.fromNumber') AS "From Number",
    json_extract_scalar(metadata,'$.toNumber') AS "To Number",
    json_extract_scalar(metadata,'$.callId') AS "Call ID",
    json_extract_scalar(metadata,'$.direction') AS Direction,
    json_extract_scalar(metadata,'$.transactionId') AS "Transaction ID"
FROM 
    "GlueDatabaseName"."call_analytics_recording_metadata"
WHERE 
    detail-subtype = "Recording"
```

## 從 voice\$1analytics\$1status 資料表中的詳細資訊 (STRUCT 資料類型） 擷取值
<a name="qry-va-status"></a>

`voice_analytics_status` 在 `struct`資料類型中具有詳細資訊欄位。下列範例示範如何查詢`struct`資料類型欄位：

```
SELECT
    detail.transactionId AS "Transaction ID",
    detail.voiceConnectorId AS "VoiceConnector ID",
    detail.siprecmetadata AS "Siprec Metadata",
    detail.inviteheaders AS "Invite Headers",
    detail.streamStartTime AS "Stream Start Time"
FROM 
    "GlueDatabaseName"."voice_analytics_status"
```

## 聯結 voice\$1analytics\$1status 和 call\$1analytics\$1metadata 資料表
<a name="qry-join-va-meta"></a>

下列範例查詢會聯結 `call_analytics_metadata`和 `voice_analytics_status`：

```
SELECT
    a.detail.transactionId AS "Transaction ID",
    a.detail.voiceConnectorId AS "VoiceConnector ID",
    a.detail.siprecmetadata AS "Siprec Metadata",
    a.detail.inviteheaders AS "Invite Headers",
    a.detail.streamStartTime AS "Stream Start Time"
    json_extract_scalar(b.metadata,'$.fromNumber') AS "From Number",
    json_extract_scalar(b.metadata,'$.toNumber') AS "To Number",
    json_extract_scalar(b.metadata,'$.callId') AS "Call ID",
    json_extract_scalar(b.metadata,'$.direction') AS Direction
FROM 
    "GlueDatabaseName"."voice_analytics_status" a
INNER JOIN 
    "GlueDatabaseName"."call_analytics_metadata" b
ON a.detail.transactionId = json_extract_scalar(b.metadata,'$.transactionId')
```

## 從 transcribe\$1call\$1analytics\$1post\$1call 資料表擷取文字記錄
<a name="qry-transcribe-ca-post-call"></a>

transcribe\$1call\$1analytics\$1post\$1call 具有具有巢狀陣列的結構格式文字記錄欄位。使用下列查詢取消巢狀陣列：

```
SELECT 
    jobstatus,
    languagecode,
    IF(CARDINALITY(m.transcript)=0 OR CARDINALITY(m.transcript) IS NULL, NULL, e.transcript.id) AS utteranceId,
    IF(CARDINALITY(m.transcript)=0 OR CARDINALITY(m.transcript) IS NULL, NULL, e.transcript.content) AS transcript,
    accountid,
    channel,
    sessionid,
    contentmetadata.output AS "Redaction"
FROM 
    "GlueDatabaseName"."transcribe_call_analytics_post_call" m
CROSS JOIN UNNEST
    (IF(CARDINALITY(m.transcript)=0, ARRAY[NULL], transcript)) AS e(transcript)
```

## 加入 transcribe\$1call\$1analytics\$1post\$1call 和 call\$1analytics\$1metadata 資料表
<a name="qry-va-status"></a>

下列查詢聯結 transcribe\$1call\$1analytics\$1post\$1call 和 call\$1analytics\$1metadata：

```
WITH metadata AS(
  SELECT 
    from_iso8601_timestamp(time) AS "Timestamp",
    date_parse(date_format(from_iso8601_timestamp(time), '%m/%d/%Y %H:%i:%s') , '%m/%d/%Y %H:%i:%s') AS "DateTime",
    date_parse(date_format(from_iso8601_timestamp(time) , '%m/%d/%Y') , '%m/%d/%Y') AS "Date",
    date_format(from_iso8601_timestamp(time) , '%H:%i:%s')  AS "Time",
    mediainsightspipelineid,
    json_extract_scalar(metadata,'$.toNumber') AS "To Number",
    json_extract_scalar(metadata,'$.voiceConnectorId') AS "VoiceConnector ID",
    json_extract_scalar(metadata,'$.fromNumber') AS "From Number",
    json_extract_scalar(metadata,'$.callId') AS "Call ID",
    json_extract_scalar(metadata,'$.direction') AS Direction,
    json_extract_scalar(metadata,'$.transactionId') AS "Transaction ID",
    REGEXP_REPLACE(REGEXP_EXTRACT(json_extract_scalar(metadata,'$.oneTimeMetadata.s3RecordingUrl'), '[^/]+(?=\.[^.]+$)'), '\.wav$', '') AS "SessionID"
  FROM 
    "GlueDatabaseName"."call_analytics_metadata"
),
transcript_events AS(
  SELECT 
    jobstatus,
    languagecode,
    IF(CARDINALITY(m.transcript)=0 OR CARDINALITY(m.transcript) IS NULL, NULL, e.transcript.id) AS utteranceId,
    IF(CARDINALITY(m.transcript)=0 OR CARDINALITY(m.transcript) IS NULL, NULL, e.transcript.content) AS transcript,
    accountid,
    channel,
    sessionid,
    contentmetadata.output AS "Redaction"
  FROM 
    "GlueDatabaseName"."transcribe_call_analytics_post_call" m
  CROSS JOIN UNNEST
    (IF(CARDINALITY(m.transcript)=0, ARRAY[NULL], transcript)) AS e(transcript)
)
SELECT 
    jobstatus,
    languagecode,
    a.utteranceId,
    transcript,
    accountid,
    channel,
    a.sessionid,
    "Redaction"
    "Timestamp",
    "DateTime",
    "Date",
    "Time",
    mediainsightspipelineid,
    "To Number",
    "VoiceConnector ID",
    "From Number",
    "Call ID",
    Direction,
    "Transaction ID"
FROM 
    "GlueDatabaseName"."transcribe_call_analytics_post_call" a
LEFT JOIN 
    metadata b
ON 
    a.sessionid = b.SessionID
```

## 查詢媒體物件 URLs以進行語音增強通話錄音
<a name="qry-voice-enhancement-call-recording"></a>

下列範例查詢會聯結 `Voice enhancement call recording` URL：

```
SELECT 
    json_extract_scalar(metadata,'$.voiceConnectorId') AS "VoiceConnector ID",
    json_extract_scalar(metadata,'$.fromNumber') AS "From Number",
    json_extract_scalar(metadata,'$.toNumber') AS "To Number",
    json_extract_scalar(metadata,'$.callId') AS "Call ID",
    json_extract_scalar(metadata,'$.direction') AS Direction,
    json_extract_scalar(metadata,'$.transactionId') AS "Transaction ID",
    s3MediaObjectConsoleUrl
FROM
    {GlueDatabaseName}."call_analytics_recording_metadata"
WHERE
    detail-subtype = "VoiceEnhancement"
```