运行查询

对结果进行分页

运行查询时，Timestream 会以分页方式返回结果集，以优化应用程序的响应能力。下面的代码片段显示了如何对结果集进行分页。必须循环浏览所有结果集页面，直到遇到空值。分页令牌在 Timestream 发放 3 小时后过期。 LiveAnalytics

注意

这些代码片段基于上的完整示例应用程序。GitHub有关如何开始使用示例应用程序的更多信息，请参阅示例应用程序。

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

以下代码段使用 for JavaScript V2 样式。 AWS SDK它基于 Node.js 示例 Amazon Timestream 中的示例 LiveAnalytics 应用程序，供其上使用。 GitHub

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());
            }
        }

解析结果集

您可以使用以下代码片段从结果集中提取数据。查询完成后，查询结果最长可在 24 小时内访问。

注意

这些代码片段基于上的完整示例应用程序。GitHub有关如何开始使用示例应用程序的更多信息，请参阅示例应用程序。

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

以下代码段使用 for JavaScript V2 样式。 AWS SDK它基于 Node.js 示例 Amazon Timestream 中的示例 LiveAnalytics 应用程序，供其上使用。 GitHub

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)}]";
        }

访问查询状态

您可以通过访问查询状态QueryResponse，其中包含有关查询进度、查询扫描的字节和查询计量的字节的信息。bytesMetered和bytesScanned值是累积的，在分页查询结果时会持续更新。您可以使用此信息来了解单个查询所扫描的字节，也可以使用它来做出某些决策。例如，假设查询价格为每扫描 GB 0.01 美元，则可能需要取消每次查询超过 25 美元或 X GB 的查询。下面的代码片段显示了如何做到这一点。

注意

这些代码片段基于上的完整示例应用程序。GitHub有关如何开始使用示例应用程序的更多信息，请参阅示例应用程序。

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

以下代码段使用 for JavaScript V2 样式。 AWS SDK它基于 Node.js 示例 Amazon Timestream 中的示例 LiveAnalytics 应用程序，供其上使用。 GitHub

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());
    }
}

有关如何取消查询的更多详细信息，请参阅取消查询。