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Append Output Mode Not Supported When There Are Streaming Aggregations On Streaming DataFrames/DataSets Without Watermark;;\nJoin Inner

I wanna join 2 streams but I received the next error and I don't know how to fix it:

Append output mode not supported when there are streaming aggregations on streaming DataFrames/DataSets without watermark;;\nJoin Inner

df_stream = spark.readStream.schema(schema_clicks).option("ignoreChanges", True).option("header", True).format("csv").load("s3://mybucket/*.csv")
display(df_stream.select("SendID", "EventType", "EventDate"))

enter image description here

I wanna join df1 with df2:

df1 = df_stream \
              .withColumn('timestamp', unix_timestamp(col('EventDate'), "MM/dd/yyyy hh:mm:ss aa").cast(TimestampType())) \
              .select(col("SendID"), col("timestamp"), col("EventType")) \
              .withColumnRenamed("SendID", "SendID_update") \
              .withColumnRenamed("timestamp", "timestamp_update") \
              .withWatermark("timestamp_update", "1 minutes")

df2 = df_stream \
              .withColumn('timestamp', unix_timestamp(col('EventDate'), "MM/dd/yyyy hh:mm:ss aa").cast(TimestampType())) \
              .withWatermark("timestamp", "1 minutes") \
              .groupBy(col("SendID")) \
              .agg(max(col('timestamp')).alias("timestamp")) \
              .orderBy('timestamp', ascending=False)

join = df2.alias("A").join(df1.alias("B"),  expr(
      "A.SendID = B.SendID_update" +
        " AND " +
        "B.timestamp_update >= A.timestamp " +
        " AND " +
        "B.timestamp_update <= A.timestamp + interval 1 hour"))

Then finally when I write the result in append mode:

join \
.writeStream \
.outputMode("Append") \
.option("checkpointLocation", "s3://checkpointjoin_delta")  \
.format("delta")  \
.table("test_join")

I received the previous error.

AnalysisException Traceback (most recent call last) in () ----> 1 join.writeStream.outputMode("Append").option("checkpointLocation", "s3://checkpointjoin_delta").format("delta").table("test_join")

/databricks/spark/python/pyspark/sql/streaming.py in table(self, tableName) 1137 """ 1138 if isinstance(tableName, basestring): -> 1139 return self._sq(self._jwrite.table(tableName)) 1140 else: 1141 raise TypeError("tableName can be only a single string")

/databricks/spark/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py in call(self, *args) 1255 answer = self.gateway_client.send_command(command) 1256 return_value = get_return_value( -> 1257 answer, self.gateway_client, self.target_id, self.name) 1258 1259 for temp_arg in temp_args:

/databricks/spark/python/pyspark/sql/utils.py in deco(*a, **kw) 67 e.java_exception.getStackTrace()))

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Answer

The problem is the .groupBy, it's necessary to add the timestamp. For example:

df2 = df_stream \
              .withColumn('timestamp', unix_timestamp(col('EventDate'), "MM/dd/yyyy hh:mm:ss aa").cast(TimestampType())) \
              .withWatermark("timestamp", "1 minutes") \
              .groupBy(col("SendID"), "timestamp") \
              .agg(max(col('timestamp')).alias("timestamp")) \
              .orderBy('timestamp', ascending=False)
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source: stackoverflow.com
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