Para recursos semelhantes aos do Amazon Timestream para, considere o Amazon Timestream LiveAnalytics para InfluxDB. Ele oferece ingestão de dados simplificada e tempos de resposta de consulta de um dígito em milissegundos para análises em tempo real. Saiba mais [aqui](https://docs.aws.amazon.com//timestream/latest/developerguide/timestream-for-influxdb.html).

As traduções são geradas por tradução automática. Em caso de conflito entre o conteúdo da tradução e da versão original em inglês, a versão em inglês prevalecerá.

# Executar consulta
<a name="code-samples.run-query"></a>

**Topics**
+ [Como paginar resultados](#code-samples.run-query.pagination)
+ [Conjuntos de resultados de análise](#code-samples.run-query.parsing)
+ [Acessar status da consulta](#code-samples.run-query.query-status)

## Como paginar resultados
<a name="code-samples.run-query.pagination"></a>

Quando você executa uma consulta, o Timestream retorna o conjunto de resultados de forma paginada para otimizar a capacidade de resposta de seus aplicativos. O trecho de código abaixo mostra como você pode paginar por meio do conjunto de resultados. Você deve percorrer todas as páginas do conjunto de resultados até encontrar um valor nulo. Os tokens de paginação expiram 3 horas após serem emitidos pelo Timestream for LiveAnalytics. 

**nota**  
Esses trechos de código são baseados em aplicativos de amostra completos no [GitHub](https://github.com/awslabs/amazon-timestream-tools/blob/master/sample_apps). Para obter mais informações sobre como criar aplicativos de amostra, consulte [Aplicação de exemplo](sample-apps.md).

------
#### [  Java  ]

```
    private void runQuery(String queryString) {
        try {
            QueryRequest queryRequest = new QueryRequest();
            queryRequest.setQueryString(queryString);
            QueryResult queryResult = queryClient.query(queryRequest);
            while (true) {
                parseQueryResult(queryResult);
                if (queryResult.getNextToken() == null) {
                    break;
                }
                queryRequest.setNextToken(queryResult.getNextToken());
                queryResult = queryClient.query(queryRequest);
            }
        } catch (Exception e) {
            // Some queries might fail with 500 if the result of a sequence function has more than 10000 entries
            e.printStackTrace();
        }
    }
```

------
#### [  Java v2  ]

```
    private void runQuery(String queryString) {
        try {
            QueryRequest queryRequest = QueryRequest.builder().queryString(queryString).build();
            final QueryIterable queryResponseIterator = timestreamQueryClient.queryPaginator(queryRequest);
            for(QueryResponse queryResponse : queryResponseIterator) {
                parseQueryResult(queryResponse);
            }
        } catch (Exception e) {
            // Some queries might fail with 500 if the result of a sequence function has more than 10000 entries
            e.printStackTrace();
        }
    }
```

------
#### [  Go  ]

```
func runQuery(queryPtr *string, querySvc *timestreamquery.TimestreamQuery, f *os.File) {
    queryInput := &timestreamquery.QueryInput{
        QueryString: aws.String(*queryPtr),
    }
    fmt.Println("QueryInput:")
    fmt.Println(queryInput)
    // execute the query
    err := querySvc.QueryPages(queryInput,
        func(page *timestreamquery.QueryOutput, lastPage bool) bool {
            // process query response
            queryStatus := page.QueryStatus
            fmt.Println("Current query status:", queryStatus)
            // query response metadata
            // includes column names and types
            metadata := page.ColumnInfo
            // fmt.Println("Metadata:")
            fmt.Println(metadata)
            header := ""
            for i := 0; i < len(metadata); i++ {
                header += *metadata[i].Name
                if i != len(metadata)-1 {
                    header += ", "
                }
            }
            write(f, header)

            // query response data
            fmt.Println("Data:")
            // process rows
            rows := page.Rows
            for i := 0; i < len(rows); i++ {
                data := rows[i].Data
                value := processRowType(data, metadata)
                fmt.Println(value)
                write(f, value)
            }
            fmt.Println("Number of rows:", len(page.Rows))
            return true
        })
    if err != nil {
        fmt.Println("Error:")
        fmt.Println(err)
    }
}
```

------
#### [  Python  ]

```
    def run_query(self, query_string):
        try:
            page_iterator = self.paginator.paginate(QueryString=query_string)
            for page in page_iterator:
                self._parse_query_result(page)
        except Exception as err:
            print("Exception while running query:", err)
```

------
#### [  Node.js  ]

O trecho de código a seguir usa o SDK AWS para o estilo for JavaScript V2. Ele se baseia no aplicativo de amostra no [aplicativo de amostra Node.js do Amazon Timestream para LiveAnalytics no GitHub](https://github.com/awslabs/amazon-timestream-tools/tree/mainline/sample_apps/js).

```
async function getAllRows(query, nextToken) {
    const params = {
        QueryString: query
    };

    if (nextToken) {
        params.NextToken = nextToken;
    }

    await queryClient.query(params).promise()
        .then(
            (response) => {
                parseQueryResult(response);
                if (response.NextToken) {
                    getAllRows(query, response.NextToken);
                }
            },
            (err) => {
                console.error("Error while querying:", err);
            });
}
```

------
#### [  .NET  ]

```
        private async Task RunQueryAsync(string queryString)
        {
            try
            {
                QueryRequest queryRequest = new QueryRequest();
                queryRequest.QueryString = queryString;
                QueryResponse queryResponse = await queryClient.QueryAsync(queryRequest);
                while (true)
                {
                    ParseQueryResult(queryResponse);
                    if (queryResponse.NextToken == null)
                    {
                        break;
                    }
                    queryRequest.NextToken = queryResponse.NextToken;
                    queryResponse = await queryClient.QueryAsync(queryRequest);
                }
            } catch(Exception e)
            {
                // Some queries might fail with 500 if the result of a sequence function has more than 10000 entries
                Console.WriteLine(e.ToString());
            }
        }
```

------

## Conjuntos de resultados de análise
<a name="code-samples.run-query.parsing"></a>

É possível usar os seguintes trechos de código para extrair dados do conjunto de resultados. Os resultados da consulta ficam disponíveis por até 24 horas após a conclusão da consulta.

**nota**  
Esses trechos de código são baseados em aplicativos de amostra completos no [GitHub](https://github.com/awslabs/amazon-timestream-tools/blob/master/sample_apps). Para obter mais informações sobre como criar aplicativos de amostra, consulte [Aplicação de exemplo](sample-apps.md).

------
#### [  Java  ]

```
    private static final DateTimeFormatter TIMESTAMP_FORMATTER = DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss.SSSSSSSSS");
    private static final DateTimeFormatter DATE_FORMATTER = DateTimeFormatter.ofPattern("yyyy-MM-dd");
    private static final DateTimeFormatter TIME_FORMATTER = DateTimeFormatter.ofPattern("HH:mm:ss.SSSSSSSSS");
    
    private static final long ONE_GB_IN_BYTES = 1073741824L;
    
    private void parseQueryResult(QueryResult response) {
        final QueryStatus currentStatusOfQuery = queryResult.getQueryStatus();
    
        System.out.println("Query progress so far: " + currentStatusOfQuery.getProgressPercentage() + "%");
        
        double bytesScannedSoFar = ((double) currentStatusOfQuery.getCumulativeBytesScanned() / ONE_GB_IN_BYTES);
        System.out.println("Bytes scanned so far: " + bytesScannedSoFar + " GB");
        
        double bytesMeteredSoFar = ((double) currentStatusOfQuery.getCumulativeBytesMetered() / ONE_GB_IN_BYTES);
        System.out.println("Bytes metered so far: " + bytesMeteredSoFar + " GB");
        
        List<ColumnInfo> columnInfo = response.getColumnInfo();
        List<Row> rows = response.getRows();
 
        System.out.println("Metadata: " + columnInfo);
        System.out.println("Data: ");
 
        // iterate every row
        for (Row row : rows) {
            System.out.println(parseRow(columnInfo, row));
        }
    }
 
    private String parseRow(List<ColumnInfo> columnInfo, Row row) {
        List<Datum> data = row.getData();
        List<String> rowOutput = new ArrayList<>();
        // iterate every column per row
        for (int j = 0; j < data.size(); j++) {
            ColumnInfo info = columnInfo.get(j);
            Datum datum = data.get(j);
            rowOutput.add(parseDatum(info, datum));
        }
        return String.format("{%s}", rowOutput.stream().map(Object::toString).collect(Collectors.joining(",")));
    }
 
    private String parseDatum(ColumnInfo info, Datum datum) {
        if (datum.isNullValue() != null && datum.isNullValue()) {
            return info.getName() + "=" + "NULL";
        }
        Type columnType = info.getType();
        // If the column is of TimeSeries Type
        if (columnType.getTimeSeriesMeasureValueColumnInfo() != null) {
            return parseTimeSeries(info, datum);
        }
        // If the column is of Array Type
        else if (columnType.getArrayColumnInfo() != null) {
            List<Datum> arrayValues = datum.getArrayValue();
            return info.getName() + "=" + parseArray(info.getType().getArrayColumnInfo(), arrayValues);
        }
        // If the column is of Row Type
        else if (columnType.getRowColumnInfo() != null) {
            List<ColumnInfo> rowColumnInfo = info.getType().getRowColumnInfo();
            Row rowValues = datum.getRowValue();
            return parseRow(rowColumnInfo, rowValues);
        }
        // If the column is of Scalar Type
        else {
            return parseScalarType(info, datum);
        }
    }
 
    private String parseTimeSeries(ColumnInfo info, Datum datum) {
        List<String> timeSeriesOutput = new ArrayList<>();
        for (TimeSeriesDataPoint dataPoint : datum.getTimeSeriesValue()) {
            timeSeriesOutput.add("{time=" + dataPoint.getTime() + ", value=" +
                    parseDatum(info.getType().getTimeSeriesMeasureValueColumnInfo(), dataPoint.getValue()) + "}");
        }
        return String.format("[%s]", timeSeriesOutput.stream().map(Object::toString).collect(Collectors.joining(",")));
    }
 
    private String parseScalarType(ColumnInfo info, Datum datum) {
        switch (ScalarType.fromValue(info.getType().getScalarType())) {
            case VARCHAR:
                return parseColumnName(info) + datum.getScalarValue();
            case BIGINT:
                Long longValue = Long.valueOf(datum.getScalarValue());
                return parseColumnName(info) + longValue;
            case INTEGER:
                Integer intValue = Integer.valueOf(datum.getScalarValue());
                return parseColumnName(info) + intValue;
            case BOOLEAN:
                Boolean booleanValue = Boolean.valueOf(datum.getScalarValue());
                return parseColumnName(info) + booleanValue;
            case DOUBLE:
                Double doubleValue = Double.valueOf(datum.getScalarValue());
                return parseColumnName(info) + doubleValue;
            case TIMESTAMP:
                return parseColumnName(info) + LocalDateTime.parse(datum.getScalarValue(), TIMESTAMP_FORMATTER);
            case DATE:
                return parseColumnName(info) + LocalDate.parse(datum.getScalarValue(), DATE_FORMATTER);
            case TIME:
                return parseColumnName(info) + LocalTime.parse(datum.getScalarValue(), TIME_FORMATTER);
            case INTERVAL_DAY_TO_SECOND:
            case INTERVAL_YEAR_TO_MONTH:
                return parseColumnName(info) + datum.getScalarValue();
            case UNKNOWN:
                return parseColumnName(info) + datum.getScalarValue();
            default:
                throw new IllegalArgumentException("Given type is not valid: " + info.getType().getScalarType());
        }
    }
 
    private String parseColumnName(ColumnInfo info) {
        return info.getName() == null ? "" : info.getName() + "=";
    }
 
    private String parseArray(ColumnInfo arrayColumnInfo, List<Datum> arrayValues) {
        List<String> arrayOutput = new ArrayList<>();
        for (Datum datum : arrayValues) {
            arrayOutput.add(parseDatum(arrayColumnInfo, datum));
        }
        return String.format("[%s]", arrayOutput.stream().map(Object::toString).collect(Collectors.joining(",")));
    }
```

------
#### [  Java v2  ]

```
    private static final long ONE_GB_IN_BYTES = 1073741824L;

    private void parseQueryResult(QueryResponse response) {
        final QueryStatus currentStatusOfQuery = response.queryStatus();

        System.out.println("Query progress so far: " + currentStatusOfQuery.progressPercentage() + "%");
        
        double bytesScannedSoFar = ((double) currentStatusOfQuery.cumulativeBytesScanned() / ONE_GB_IN_BYTES);
        System.out.println("Bytes scanned so far: " + bytesScannedSoFar + " GB");
        
        double bytesMeteredSoFar = ((double) currentStatusOfQuery.cumulativeBytesMetered() / ONE_GB_IN_BYTES);
        System.out.println("Bytes metered so far: " + bytesMeteredSoFar + " GB");
        
        List<ColumnInfo> columnInfo = response.columnInfo();
        List<Row> rows = response.rows();

        System.out.println("Metadata: " + columnInfo);
        System.out.println("Data: ");

        // iterate every row
        for (Row row : rows) {
            System.out.println(parseRow(columnInfo, row));
        }
    }

    private String parseRow(List<ColumnInfo> columnInfo, Row row) {
        List<Datum> data = row.data();
        List<String> rowOutput = new ArrayList<>();
        // iterate every column per row
        for (int j = 0; j < data.size(); j++) {
            ColumnInfo info = columnInfo.get(j);
            Datum datum = data.get(j);
            rowOutput.add(parseDatum(info, datum));
        }
        return String.format("{%s}", rowOutput.stream().map(Object::toString).collect(Collectors.joining(",")));
    }

    private String parseDatum(ColumnInfo info, Datum datum) {
        if (datum.nullValue() != null && datum.nullValue()) {
            return info.name() + "=" + "NULL";
        }
        Type columnType = info.type();
        // If the column is of TimeSeries Type
        if (columnType.timeSeriesMeasureValueColumnInfo() != null) {
            return parseTimeSeries(info, datum);
        }
        // If the column is of Array Type
        else if (columnType.arrayColumnInfo() != null) {
            List<Datum> arrayValues = datum.arrayValue();
            return info.name() + "=" + parseArray(info.type().arrayColumnInfo(), arrayValues);
        }
        // If the column is of Row Type
        else if (columnType.rowColumnInfo() != null && columnType.rowColumnInfo().size() > 0) {
            List<ColumnInfo> rowColumnInfo = info.type().rowColumnInfo();
            Row rowValues = datum.rowValue();
            return parseRow(rowColumnInfo, rowValues);
        }
        // If the column is of Scalar Type
        else {
            return parseScalarType(info, datum);
        }
    }

    private String parseTimeSeries(ColumnInfo info, Datum datum) {
        List<String> timeSeriesOutput = new ArrayList<>();
        for (TimeSeriesDataPoint dataPoint : datum.timeSeriesValue()) {
            timeSeriesOutput.add("{time=" + dataPoint.time() + ", value=" +
                    parseDatum(info.type().timeSeriesMeasureValueColumnInfo(), dataPoint.value()) + "}");
        }
        return String.format("[%s]", timeSeriesOutput.stream().map(Object::toString).collect(Collectors.joining(",")));
    }

    private String parseScalarType(ColumnInfo info, Datum datum) {
        return parseColumnName(info) + datum.scalarValue();
    }

    private String parseColumnName(ColumnInfo info) {
        return info.name() == null ? "" : info.name() + "=";
    }

    private String parseArray(ColumnInfo arrayColumnInfo, List<Datum> arrayValues) {
        List<String> arrayOutput = new ArrayList<>();
        for (Datum datum : arrayValues) {
            arrayOutput.add(parseDatum(arrayColumnInfo, datum));
        }
        return String.format("[%s]", arrayOutput.stream().map(Object::toString).collect(Collectors.joining(",")));
    }
```

------
#### [  Go  ]

```
func processScalarType(data *timestreamquery.Datum) string {
    return *data.ScalarValue
}

func processTimeSeriesType(data []*timestreamquery.TimeSeriesDataPoint, columnInfo *timestreamquery.ColumnInfo) string {
    value := ""
    for k := 0; k < len(data); k++ {
        time := data[k].Time
        value += *time + ":"
        if columnInfo.Type.ScalarType != nil {
            value += processScalarType(data[k].Value)
        } else if columnInfo.Type.ArrayColumnInfo != nil {
            value += processArrayType(data[k].Value.ArrayValue, columnInfo.Type.ArrayColumnInfo)
        } else if columnInfo.Type.RowColumnInfo != nil {
            value += processRowType(data[k].Value.RowValue.Data, columnInfo.Type.RowColumnInfo)
        } else {
            fail("Bad data type")
        }
        if k != len(data)-1 {
            value += ", "
        }
    }
    return value
}

func processArrayType(datumList []*timestreamquery.Datum, columnInfo *timestreamquery.ColumnInfo) string {
    value := ""
    for k := 0; k < len(datumList); k++ {
        if columnInfo.Type.ScalarType != nil {
            value += processScalarType(datumList[k])
        } else if columnInfo.Type.TimeSeriesMeasureValueColumnInfo != nil {
            value += processTimeSeriesType(datumList[k].TimeSeriesValue, columnInfo.Type.TimeSeriesMeasureValueColumnInfo)
        } else if columnInfo.Type.ArrayColumnInfo != nil {
            value += "["
            value += processArrayType(datumList[k].ArrayValue, columnInfo.Type.ArrayColumnInfo)
            value += "]"
        } else if columnInfo.Type.RowColumnInfo != nil {
            value += "["
            value += processRowType(datumList[k].RowValue.Data, columnInfo.Type.RowColumnInfo)
            value += "]"
        } else {
            fail("Bad column type")
        }

        if k != len(datumList)-1 {
            value += ", "
        }
    }
    return value
}

func processRowType(data []*timestreamquery.Datum, metadata []*timestreamquery.ColumnInfo) string {
    value := ""
    for j := 0; j < len(data); j++ {
        if metadata[j].Type.ScalarType != nil {
            // process simple data types
            value += processScalarType(data[j])
        } else if metadata[j].Type.TimeSeriesMeasureValueColumnInfo != nil {
            // fmt.Println("Timeseries measure value column info")
            // fmt.Println(metadata[j].Type.TimeSeriesMeasureValueColumnInfo.Type)
            datapointList := data[j].TimeSeriesValue
            value += "["
            value += processTimeSeriesType(datapointList, metadata[j].Type.TimeSeriesMeasureValueColumnInfo)
            value += "]"
        } else if metadata[j].Type.ArrayColumnInfo != nil {
            columnInfo := metadata[j].Type.ArrayColumnInfo
            // fmt.Println("Array column info")
            // fmt.Println(columnInfo)
            datumList := data[j].ArrayValue
            value += "["
            value += processArrayType(datumList, columnInfo)
            value += "]"
        } else if metadata[j].Type.RowColumnInfo != nil {
            columnInfo := metadata[j].Type.RowColumnInfo
            datumList := data[j].RowValue.Data
            value += "["
            value += processRowType(datumList, columnInfo)
            value += "]"
        } else {
            panic("Bad column type")
        }
        // comma seperated column values
        if j != len(data)-1 {
            value += ", "
        }
    }
    return value
}
```

------
#### [  Python  ]

```
    def _parse_query_result(self, query_result):
        query_status = query_result["QueryStatus"]

        progress_percentage = query_status["ProgressPercentage"]
        print(f"Query progress so far: {progress_percentage}%")

        bytes_scanned = float(query_status["CumulativeBytesScanned"]) / ONE_GB_IN_BYTES
        print(f"Data scanned so far: {bytes_scanned} GB")

        bytes_metered = float(query_status["CumulativeBytesMetered"]) / ONE_GB_IN_BYTES
        print(f"Data metered so far: {bytes_metered} GB")

        column_info = query_result['ColumnInfo']

        print("Metadata: %s" % column_info)
        print("Data: ")
        for row in query_result['Rows']:
            print(self._parse_row(column_info, row))

    def _parse_row(self, column_info, row):
        data = row['Data']
        row_output = []
        for j in range(len(data)):
            info = column_info[j]
            datum = data[j]
            row_output.append(self._parse_datum(info, datum))

        return "{%s}" % str(row_output)

    def _parse_datum(self, info, datum):
        if datum.get('NullValue', False):
            return "%s=NULL" % info['Name'],

        column_type = info['Type']

        # If the column is of TimeSeries Type
        if 'TimeSeriesMeasureValueColumnInfo' in column_type:
            return self._parse_time_series(info, datum)

        # If the column is of Array Type
        elif 'ArrayColumnInfo' in column_type:
            array_values = datum['ArrayValue']
            return "%s=%s" % (info['Name'], self._parse_array(info['Type']['ArrayColumnInfo'], array_values))

        # If the column is of Row Type
        elif 'RowColumnInfo' in column_type:
            row_column_info = info['Type']['RowColumnInfo']
            row_values = datum['RowValue']
            return self._parse_row(row_column_info, row_values)

        # If the column is of Scalar Type
        else:
            return self._parse_column_name(info) + datum['ScalarValue']

    def _parse_time_series(self, info, datum):
        time_series_output = []
        for data_point in datum['TimeSeriesValue']:
            time_series_output.append("{time=%s, value=%s}"
                                      % (data_point['Time'],
                                         self._parse_datum(info['Type']['TimeSeriesMeasureValueColumnInfo'],
                                                           data_point['Value'])))
        return "[%s]" % str(time_series_output)

    def _parse_array(self, array_column_info, array_values):
        array_output = []
        for datum in array_values:
            array_output.append(self._parse_datum(array_column_info, datum))

        return "[%s]" % str(array_output)
        
    @staticmethod
    def _parse_column_name(info):
        if 'Name' in info:
            return info['Name'] + "="
        else:
            return ""
```

------
#### [  Node.js  ]

O trecho de código a seguir usa o SDK AWS para o estilo for JavaScript V2. Ele se baseia no aplicativo de amostra no [aplicativo de amostra Node.js do Amazon Timestream para LiveAnalytics no GitHub](https://github.com/awslabs/amazon-timestream-tools/tree/mainline/sample_apps/js).

```
function parseQueryResult(response) {
    const queryStatus = response.QueryStatus;
    console.log("Current query status: " + JSON.stringify(queryStatus));
    
    const columnInfo = response.ColumnInfo;
    const rows = response.Rows;

    console.log("Metadata: " + JSON.stringify(columnInfo));
    console.log("Data: ");

    rows.forEach(function (row) {
        console.log(parseRow(columnInfo, row));
    });
}

function parseRow(columnInfo, row) {
    const data = row.Data;
    const rowOutput = [];

    var i;
    for ( i = 0; i < data.length; i++ ) {
        info = columnInfo[i];
        datum = data[i];
        rowOutput.push(parseDatum(info, datum));
    }

    return `{${rowOutput.join(", ")}}`
}

function parseDatum(info, datum) {
    if (datum.NullValue != null && datum.NullValue === true) {
        return `${info.Name}=NULL`;
    }

    const columnType = info.Type;

    // If the column is of TimeSeries Type
    if (columnType.TimeSeriesMeasureValueColumnInfo != null) {
        return parseTimeSeries(info, datum);
    }
    // If the column is of Array Type
    else if (columnType.ArrayColumnInfo != null) {
        const arrayValues = datum.ArrayValue;
        return `${info.Name}=${parseArray(info.Type.ArrayColumnInfo, arrayValues)}`;
    }
    // If the column is of Row Type
    else if (columnType.RowColumnInfo != null) {
        const rowColumnInfo = info.Type.RowColumnInfo;
        const rowValues = datum.RowValue;
        return parseRow(rowColumnInfo, rowValues);
    }
    // If the column is of Scalar Type
    else {
        return parseScalarType(info, datum);
    }
}

function parseTimeSeries(info, datum) {
    const timeSeriesOutput = [];
    datum.TimeSeriesValue.forEach(function (dataPoint) {
        timeSeriesOutput.push(`{time=${dataPoint.Time}, value=${parseDatum(info.Type.TimeSeriesMeasureValueColumnInfo, dataPoint.Value)}}`)
    });

    return `[${timeSeriesOutput.join(", ")}]`
}

function parseScalarType(info, datum) {
    return parseColumnName(info) + datum.ScalarValue;
}

function parseColumnName(info) {
    return info.Name == null ? "" : `${info.Name}=`;
}

function parseArray(arrayColumnInfo, arrayValues) {
    const arrayOutput = [];
    arrayValues.forEach(function (datum) {
        arrayOutput.push(parseDatum(arrayColumnInfo, datum));
    });
    return `[${arrayOutput.join(", ")}]`
}
```

------
#### [  .NET  ]

```
        private void ParseQueryResult(QueryResponse response)
        {
            List<ColumnInfo> columnInfo = response.ColumnInfo;
            var options = new JsonSerializerOptions
            {
                IgnoreNullValues = true
            };
            List<String> columnInfoStrings = columnInfo.ConvertAll(x => JsonSerializer.Serialize(x, options));
            List<Row> rows = response.Rows;
            
            QueryStatus queryStatus = response.QueryStatus;
            Console.WriteLine("Current Query status:" + JsonSerializer.Serialize(queryStatus, options));
            
            Console.WriteLine("Metadata:" + string.Join(",", columnInfoStrings));
            Console.WriteLine("Data:");

            foreach (Row row in rows)
            {
                Console.WriteLine(ParseRow(columnInfo, row));
            }
        }

        private string ParseRow(List<ColumnInfo> columnInfo, Row row)
        {
            List<Datum> data = row.Data;
            List<string> rowOutput = new List<string>();
            for (int j = 0; j < data.Count; j++)
            {
                ColumnInfo info = columnInfo[j];
                Datum datum = data[j];
                rowOutput.Add(ParseDatum(info, datum));
            }
            return $"{{{string.Join(",", rowOutput)}}}";
        }

        private string ParseDatum(ColumnInfo info, Datum datum)
        {
            if (datum.NullValue)
            {
                return $"{info.Name}=NULL";
            }

            Amazon.TimestreamQuery.Model.Type columnType = info.Type;
            if (columnType.TimeSeriesMeasureValueColumnInfo != null)
            {
                return ParseTimeSeries(info, datum);
            }
            else if (columnType.ArrayColumnInfo != null)
            {
                List<Datum> arrayValues = datum.ArrayValue;
                return $"{info.Name}={ParseArray(info.Type.ArrayColumnInfo, arrayValues)}";
            }
            else if (columnType.RowColumnInfo != null && columnType.RowColumnInfo.Count > 0)
            {
                List<ColumnInfo> rowColumnInfo = info.Type.RowColumnInfo;
                Row rowValue = datum.RowValue;
                return ParseRow(rowColumnInfo, rowValue);
            }
            else
            {
                return ParseScalarType(info, datum);
            }
        }

        private string ParseTimeSeries(ColumnInfo info, Datum datum)
        {
            var timeseriesString = datum.TimeSeriesValue
                .Select(value => $"{{time={value.Time}, value={ParseDatum(info.Type.TimeSeriesMeasureValueColumnInfo, value.Value)}}}")
                .Aggregate((current, next) => current + "," + next);

            return $"[{timeseriesString}]";
        }

        private string ParseScalarType(ColumnInfo info, Datum datum)
        {
            return ParseColumnName(info) + datum.ScalarValue;
        }

        private string ParseColumnName(ColumnInfo info)
        {
            return info.Name == null ? "" : (info.Name + "=");
        }

        private string ParseArray(ColumnInfo arrayColumnInfo, List<Datum> arrayValues)
        {
            return $"[{arrayValues.Select(value => ParseDatum(arrayColumnInfo, value)).Aggregate((current, next) => current + "," + next)}]";
        }
```

------

## Acessar status da consulta
<a name="code-samples.run-query.query-status"></a>

 Você pode acessar o status da consulta por meio de `QueryResponse`, que contém informações sobre o progresso de uma consulta, os bytes verificados por uma consulta e os bytes medidos por uma consulta. Os valores `bytesMetered` e `bytesScanned` são cumulativos e atualizados continuamente durante a paginação dos resultados da consulta. É possível usar essas informações para entender os bytes digitalizados por uma consulta individual e também usá-las para tomar determinadas decisões. Por exemplo, supondo que o preço da consulta seja de 0,01 USD por GB digitalizado, talvez você queira cancelar consultas que excedam 25 USD por consulta ou GB. `X` O trecho de código abaixo mostra como isso pode ser feito. 

**nota**  
Esses trechos de código são baseados em aplicativos de amostra completos no [GitHub](https://github.com/awslabs/amazon-timestream-tools/blob/master/sample_apps). Para obter mais informações sobre como criar aplicativos de amostra, consulte [Aplicação de exemplo](sample-apps.md).

------
#### [  Java  ]

```
    private static final long ONE_GB_IN_BYTES = 1073741824L;
    private static final double QUERY_COST_PER_GB_IN_DOLLARS = 0.01; // Assuming the price of query is $0.01 per GB

    public void cancelQueryBasedOnQueryStatus() {
        System.out.println("Starting query: " + SELECT_ALL_QUERY);
        QueryRequest queryRequest = new QueryRequest();
        queryRequest.setQueryString(SELECT_ALL_QUERY);
        QueryResult queryResult = queryClient.query(queryRequest);

        while (true) {
            final QueryStatus currentStatusOfQuery = queryResult.getQueryStatus();
            System.out.println("Query progress so far: " + currentStatusOfQuery.getProgressPercentage() + "%");
            double bytesMeteredSoFar = ((double) currentStatusOfQuery.getCumulativeBytesMetered() / ONE_GB_IN_BYTES);
            System.out.println("Bytes metered so far: " + bytesMeteredSoFar + " GB");
            // Cancel query if its costing more than 1 cent
            if (bytesMeteredSoFar * QUERY_COST_PER_GB_IN_DOLLARS > 0.01) {
                cancelQuery(queryResult);
                break;
            }

            if (queryResult.getNextToken() == null) {
                break;
            }
            queryRequest.setNextToken(queryResult.getNextToken());
            queryResult = queryClient.query(queryRequest);
        }
    }
```

------
#### [  Java v2  ]

```
    private static final long ONE_GB_IN_BYTES = 1073741824L;
    private static final double QUERY_COST_PER_GB_IN_DOLLARS = 0.01; // Assuming the price of query is $0.01 per GB

    public void cancelQueryBasedOnQueryStatus() {
        System.out.println("Starting query: " + SELECT_ALL_QUERY);
        QueryRequest queryRequest = QueryRequest.builder().queryString(SELECT_ALL_QUERY).build();

        final QueryIterable queryResponseIterator = timestreamQueryClient.queryPaginator(queryRequest);
        for(QueryResponse queryResponse : queryResponseIterator) {
            final QueryStatus currentStatusOfQuery = queryResponse.queryStatus();
            System.out.println("Query progress so far: " + currentStatusOfQuery.progressPercentage() + "%");
            double bytesMeteredSoFar = ((double) currentStatusOfQuery.cumulativeBytesMetered() / ONE_GB_IN_BYTES);
            System.out.println("Bytes metered so far: " + bytesMeteredSoFar + "GB");
            // Cancel query if its costing more than 1 cent
            if (bytesMeteredSoFar * QUERY_COST_PER_GB_IN_DOLLARS > 0.01) {
                cancelQuery(queryResponse);
                break;
            }
        }
    }
```

------
#### [  Go  ]

```
const OneGbInBytes = 1073741824
// Assuming the price of query is $0.01 per GB
const QueryCostPerGbInDollars = 0.01

func cancelQueryBasedOnQueryStatus(queryPtr *string, querySvc *timestreamquery.TimestreamQuery, f *os.File) {
    queryInput := &timestreamquery.QueryInput{
        QueryString: aws.String(*queryPtr),
    }
    fmt.Println("QueryInput:")
    fmt.Println(queryInput)
    // execute the query
    err := querySvc.QueryPages(queryInput,
        func(page *timestreamquery.QueryOutput, lastPage bool) bool {
            // process query response
            queryStatus := page.QueryStatus
            fmt.Println("Current query status:", queryStatus)
            bytes_metered := float64(*queryStatus.CumulativeBytesMetered) / float64(ONE_GB_IN_BYTES)
            if bytes_metered * QUERY_COST_PER_GB_IN_DOLLARS > 0.01 {
                cancelQuery(page, querySvc)
                return true
            }
            // query response metadata
            // includes column names and types
            metadata := page.ColumnInfo
            // fmt.Println("Metadata:")
            fmt.Println(metadata)
            header := ""
            for i := 0; i < len(metadata); i++ {
                header += *metadata[i].Name
                if i != len(metadata)-1 {
                    header += ", "
                }
            }
            write(f, header)

            // query response data
            fmt.Println("Data:")
            // process rows
            rows := page.Rows
            for i := 0; i < len(rows); i++ {
                data := rows[i].Data
                value := processRowType(data, metadata)
                fmt.Println(value)
                write(f, value)
            }
            fmt.Println("Number of rows:", len(page.Rows))
            return true
        })
    if err != nil {
        fmt.Println("Error:")
        fmt.Println(err)
    }
}
```

------
#### [  Python  ]

```
ONE_GB_IN_BYTES = 1073741824
# Assuming the price of query is $0.01 per GB
QUERY_COST_PER_GB_IN_DOLLARS = 0.01 

    def cancel_query_based_on_query_status(self):
        try:
            print("Starting query: " + self.SELECT_ALL)
            page_iterator = self.paginator.paginate(QueryString=self.SELECT_ALL)
            for page in page_iterator:
                query_status = page["QueryStatus"]
                progress_percentage = query_status["ProgressPercentage"]
                print("Query progress so far: " + str(progress_percentage) + "%")
                bytes_metered = query_status["CumulativeBytesMetered"] / self.ONE_GB_IN_BYTES
                print("Bytes Metered so far: " + str(bytes_metered) + " GB")
                if bytes_metered * self.QUERY_COST_PER_GB_IN_DOLLARS > 0.01:
                    self.cancel_query_for(page)
                    break
        except Exception as err:
            print("Exception while running query:", err)
            traceback.print_exc(file=sys.stderr)
```

------
#### [  Node.js  ]

O trecho de código a seguir usa o SDK AWS para o estilo for JavaScript V2. Ele se baseia no aplicativo de amostra no [aplicativo de amostra Node.js do Amazon Timestream para LiveAnalytics no GitHub](https://github.com/awslabs/amazon-timestream-tools/tree/mainline/sample_apps/js).

```
function parseQueryResult(response) {
    const queryStatus = response.QueryStatus;
    console.log("Current query status: " + JSON.stringify(queryStatus));
    
    const columnInfo = response.ColumnInfo;
    const rows = response.Rows;

    console.log("Metadata: " + JSON.stringify(columnInfo));
    console.log("Data: ");

    rows.forEach(function (row) {
        console.log(parseRow(columnInfo, row));
    });
}

function parseRow(columnInfo, row) {
    const data = row.Data;
    const rowOutput = [];

    var i;
    for ( i = 0; i < data.length; i++ ) {
        info = columnInfo[i];
        datum = data[i];
        rowOutput.push(parseDatum(info, datum));
    }

    return `{${rowOutput.join(", ")}}`
}

function parseDatum(info, datum) {
    if (datum.NullValue != null && datum.NullValue === true) {
        return `${info.Name}=NULL`;
    }

    const columnType = info.Type;

    // If the column is of TimeSeries Type
    if (columnType.TimeSeriesMeasureValueColumnInfo != null) {
        return parseTimeSeries(info, datum);
    }
    // If the column is of Array Type
    else if (columnType.ArrayColumnInfo != null) {
        const arrayValues = datum.ArrayValue;
        return `${info.Name}=${parseArray(info.Type.ArrayColumnInfo, arrayValues)}`;
    }
    // If the column is of Row Type
    else if (columnType.RowColumnInfo != null) {
        const rowColumnInfo = info.Type.RowColumnInfo;
        const rowValues = datum.RowValue;
        return parseRow(rowColumnInfo, rowValues);
    }
    // If the column is of Scalar Type
    else {
        return parseScalarType(info, datum);
    }
}

function parseTimeSeries(info, datum) {
    const timeSeriesOutput = [];
    datum.TimeSeriesValue.forEach(function (dataPoint) {
        timeSeriesOutput.push(`{time=${dataPoint.Time}, value=${parseDatum(info.Type.TimeSeriesMeasureValueColumnInfo, dataPoint.Value)}}`)
    });

    return `[${timeSeriesOutput.join(", ")}]`
}

function parseScalarType(info, datum) {
    return parseColumnName(info) + datum.ScalarValue;
}

function parseColumnName(info) {
    return info.Name == null ? "" : `${info.Name}=`;
}

function parseArray(arrayColumnInfo, arrayValues) {
    const arrayOutput = [];
    arrayValues.forEach(function (datum) {
        arrayOutput.push(parseDatum(arrayColumnInfo, datum));
    });
    return `[${arrayOutput.join(", ")}]`
}
```

------
#### [  .NET  ]

```
private static readonly long ONE_GB_IN_BYTES = 1073741824L;
private static readonly double QUERY_COST_PER_GB_IN_DOLLARS = 0.01; // Assuming the price of query is $0.01 per GB

private async Task CancelQueryBasedOnQueryStatus(string queryString)
{
    try
    {
        QueryRequest queryRequest = new QueryRequest();
        queryRequest.QueryString = queryString;
        QueryResponse queryResponse = await queryClient.QueryAsync(queryRequest);
        while (true)
        {
            QueryStatus queryStatus = queryResponse.QueryStatus;
            double bytesMeteredSoFar = ((double) queryStatus.CumulativeBytesMetered / ONE_GB_IN_BYTES);
            // Cancel query if its costing more than 1 cent
            if (bytesMeteredSoFar * QUERY_COST_PER_GB_IN_DOLLARS > 0.01)
            {
                await CancelQuery(queryResponse);
                break;
            }

            ParseQueryResult(queryResponse);
            if (queryResponse.NextToken == null)
            {
                break;
            }
            queryRequest.NextToken = queryResponse.NextToken;
            queryResponse = await queryClient.QueryAsync(queryRequest);
       }
    } catch(Exception e)
    {
        // Some queries might fail with 500 if the result of a sequence function has more than 10000 entries
        Console.WriteLine(e.ToString());
    }
}
```

------

 Para obter detalhes adicionais sobre como cancelar uma consulta, consulte [Cancelar consulta](code-samples.cancel-query.md).