一种可能的解决方法是使用GCPSQLSourceConnector。这是一个Google Cloud Pub/Sub源连接器，可在Spark Streaming和Structured Streaming中使用，以从Pub/Sub主题读取数据。以下是使用GCPSQLSourceConnector读取数据的示例代码：

import com.google.cloud.spark.pubsub._ import org.apache.spark.sql.functions._ import org.apache.spark.sql.streaming.Trigger

val spark = SparkSession.builder.appName("PubSub").getOrCreate()

// set up Pub/Sub subscription configuration val subscription = "projects/YOUR_PROJECT_ID/subscriptions/YOUR_SUBSCRIPTION_NAME" val startingOffset = "latest"

// define streaming dataframe read options val pubsubOptions = Map( "subscription" -> subscription, "startingOffset" -> startingOffset)

// create stream from Pub/Sub val pubsubStream = spark .readStream .format("pubsub") .options(pubsubOptions) .load()

// manipulate the stream as desired val words = pubsubStream .select(from_json(col("data").cast("string"), Map("text" -> "string")).alias("parsed")) .select("parsed.*", explode(split(col("text"), " ")).alias("word")) .groupBy("word").count()

// set up the stream output configuration val outputPath = "gs://YOUR_BUCKET/YOUR_OUTPUT_PATH" val checkpointPath = "gs://YOUR_BUCKET/checkpoints"

// write the stream to output location words .writeStream .format("parquet") .option("path", outputPath) .option("checkpointLocation", checkpointPath) .trigger(Trigger.ProcessingTime("20 seconds")) .start()

这个示例展示了如何使用GCPSQLSourceConnector从Pub/Sub主题读取数据并将数据写入Parquet文件输出。首先，您需要设置订阅和起始偏移量。接下来，将这些选项传递给Spark读取流。为了演示如何处理主题中的消息，我们使用from_json和explode函数，然后使用groupBy函数计算每个单词的出现次数。最后，我们定义输出和检