```pythonimport randomimport timeimport requestsSENSOR_API_ENDPOINT = "http://localhost:8000/api/sensor-data"def data_generator(): # 模拟传感器数据生成 while True: temperature = random.uniform(20, 40) humidity = random.uniform(30, 60) yield temperature, humidity time.sleep(1)def process_data(temperature, humidity): # 数据处理函数 if temperature > 30: ...
Number of threads: 1000Initializing random number generator from current timeInitializing worker threads...FATAL: mysql_stmt_prepare() failedFATAL: MySQL error: 1461 "Can't create more than max_prepared_stmt_count statements (current value: 16382)"```如果我们将--threads 调整的小一些是没有问题的。出现这个报错与 "**max_prepared_stmt_count**[1]" 参数有关,该参数取值范围为0~1048576,默认为“...
Number of threads: 1000Initializing random number generator from current timeInitializing worker threads...FATAL: mysql_stmt_prepare() failedFATAL: MySQL error: 1461 "Can't create more than max_prepared_stmt_count statements (current value: 16382)"```如果我们将--threads 调整的小一些是没有问题的。出现这个报错与 "**max_prepared_stmt_count**[1]" 参数有关,该参数取值范围为0~1048576,默认为“...
const optionalNumber = Math.floor(Math.random() * 30) + 1; // 生成红球号码 const redBallNumber = []; while (redBallNumber.length < 6) { ... GAN 是由两个部分构成的:生成器(Generator)和判别器(Discriminator)。生成器的任务是生成看起来真实的数据,而判别器的任务是判断数据是否是真实的。 那么,GAN 是如何工作的呢?它的工作原理可以比作一场“真假...
import org.apache.flink.streaming.api.functions.source.datagen.DataGeneratorSource;import org.apache.flink.table.api.DataTypes;import org.apache.flink.table.catalog.Column;import org.apache.flink.table.data.RowData;import org.apache.flink.table.types.DataType;import java.time.Duration;import java.time.LocalDateTime;import java.util.Arrays;import java.util.List;import java.util.Random;public clas...
Number of threads: 1000Initializing random number generator from current timeInitializing worker threads...FATAL: mysql_stmt_prepare() failedFATAL: MySQL error: 1461 "Can't create more than max_prepared_stmt_count statements (current value: 16382)"```如果我们将--threads 调整的小一些是没有问题的。出现这个报错与 "**max_prepared_stmt_count**[1]" 参数有关,该参数取值范围为0~1048576,默认为“...
qint64 nonceNum = QRandomGenerator::global()->generate64() % 99999999; //随机生成 0 到 9 的随机数postDataObj["nonce"] = nonceNum;// timestampqint64 curUtcSeconds = QDateTime::currentSecsSinceEpoch();postDataObj["timestamp"] = curUtcSeconds;// digestQString digestData = key;digestData += QString::number(nonceNum);digestData += QString::number(curUtcSeconds);digestData += authMsg;QString sign = Q...
const optionalNumber = Math.floor(Math.random() * 30) + 1; // 生成红球号码 const redBallNumber = []; while (redBallNumber.length < 6) { ... GAN 是由两个部分构成的:生成器(Generator)和判别器(Discriminator)。生成器的任务是生成看起来真实的数据,而判别器的任务是判断数据是否是真实的。 那么,GAN 是如何工作的呢?它的工作原理可以比作一场“真假...
本例子中指定datagen rows-per-second 选填 10000 Long 每秒发送数据的速度 number-of-rows 选填 Long 要发送的行总数,默认情况下,是无界的 fields..kind 选填 random String sequence或random fields..min 选填 随机生成器的最小值,适用于数字类型 fields..max 选填 随机生成器的最大值,适用于数字类型 fields..max-past 选填 0 Duration Maximum past of timestamp random generator, only works for timestamp types. fi...
Generator.getRandomNum ( 0, 10000000 ) ); param.put ( "category", "主动被动事件" ); param.put ( "author_id", Generator.getRandomNum ( 0, 10000 ) ); param.put ( "$inline", "true" ); JSONArray uuid_list = new JSONArray (); for (int i = 0; i
Generator.getRandomNum ( 0, 10000000 ) ); param.put ( "category", "主动被动事件" ); param.put ( "author_id", Generator.getRandomNum ( 0, 10000 ) ); param.put ( "$inline", "true" ); JSONArray uuid_list = new JSONArray (); for (int i = 0; i
Generator.getRandomNum ( 0, 10000000 ) ); param.put ( "category", "主动被动事件" ); param.put ( "author_id", Generator.getRandomNum ( 0, 10000 ) ); param.put ( "$inline", "true" ); JSONArray uuid_list = new JSONArray (); for (int i = 0; i
Generator.getRandomNum ( 0, 10000000 ) ); param.put ( "category", "主动被动事件" ); param.put ( "author_id", Generator.getRandomNum ( 0, 10000 ) ); param.put ( "$inline", "true" ); JSONArray uuid_list = new JSONArray (); for (int i = 0; i