Beispielabfragen - Amazon Chime SDK

Die vorliegende Übersetzung wurde maschinell erstellt. Im Falle eines Konflikts oder eines Widerspruchs zwischen dieser übersetzten Fassung und der englischen Fassung (einschließlich infolge von Verzögerungen bei der Übersetzung) ist die englische Fassung maßgeblich.

Beispielabfragen

Verwenden Sie diese Beispielabfragen, um die Daten in Ihrem Glue-Datenkatalog für Amazon Chime SDK Call Analytics zu extrahieren und zu organisieren.

Anmerkung

Informationen zum Herstellen einer Verbindung mit Amazon Athena und zum Abfragen Ihres Glue-Datenkatalogs finden Sie unter Herstellen einer Verbindung zu Amazon Athena mit ODBC.

Erweitern Sie jeden Abschnitt nach Bedarf.

call_analytics_metadatahat das Feld in einem JSON-Zeichenkettenformat. metadata Verwenden Sie die Funktion json_extract_scalar in Athena, um die Elemente in dieser Zeichenfolge abzufragen.

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"

Das Feld enthält das call_analytics_metadata Metadatenfeld in einem JSON-String-Format. metadatahat ein anderes verschachteltes Objekt namensoneTimeMetadata. Dieses Objekt enthält SIPrec-Metadaten im ursprünglichen XML- und transformierten JSON-Format. Verwenden Sie die json_extract_scalar Funktion in Athena, um die Elemente in dieser Zeichenfolge abzufragen.

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";

call_analytics_recording_metadatahat das Metadatenfeld in einem JSON-String-Format. Verwenden Sie die Funktion json_extract_scalar in Athena, um die Elemente in dieser Zeichenfolge abzufragen.

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"

voice_analytics_statushat ein Detailfeld im Datentyp. struct Das folgende Beispiel zeigt, wie ein struct Datentypfeld abgefragt wird:

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"

Die folgende Beispielabfrage verknüpft und: call_analytics_metadata 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')

transcribe_call_analytics_post_call hat ein Transkriptfeld im Strukturformat mit verschachtelten Arrays. Verwenden Sie die folgende Abfrage, um die Verschachtelung der Arrays aufzuheben:

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)

Die folgende Abfrage verbindet transcribe_call_analytics_post_call und call_analytics_metadata:

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

Die folgende Beispielabfrage verknüpft die 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"