Best to qualify your thoughts with an example
By using the foreachBatch function combined with the update output mode in
Spark Structured Streaming, you can effectively handle and integrate
late-arriving data into your aggregations. This approach will allow you to
continuously update your aggregated results with both on-time and late data
example
from pyspark.sql import SparkSession
from pyspark.sql.functions import expr, col, window, sum as spark_sum, max
as spark_max, current_timestamp
# Create Spark session
spark = SparkSession.builder.appName("exampleWithRate").getOrCreate()
sc = spark.sparkContext
sc.setLogLevel("ERROR")
# Simulate a stream of data with an event time
stream = spark.readStream.format("rate").option("rowsPerSecond", 5).load()
base_timestamp = current_timestamp()
stream = stream.withColumn("event_time", (base_timestamp + (col("value") *
60).cast("interval second")).cast("timestamp"))
stream = stream.withColumn("value", col("value") % 10)
def process_batch(batch_df, batch_id):
# Read current state from an external store (simulated here as a static
DataFrame)
current_state = spark.createDataFrame(
[(1, 10, '2024-06-13 10:00:00')],
["key", "total_value", "max_event_time"]
).withColumn("max_event_time", col("max_event_time").cast("timestamp"))
# Perform aggregation including late data handling
aggregated_batch = batch_df.groupBy("value").agg(
spark_sum("value").alias("total_value"),
spark_max("event_time").alias("max_event_time")
)
# Merge with current state
merged_state = current_state.union(aggregated_batch)
# Show the merged state
merged_state.show(truncate=False)
# Define your streaming query
streaming_query = (
stream
.withWatermark("event_time", "10 minutes")
.writeStream
.foreachBatch(process_batch)
.outputMode("update")
.start()
)
# Await termination
streaming_query.awaitTermination()
and the output
+---+-----------+-------------------+
|key|total_value|max_event_time |
+---+-----------+-------------------+
|1 |10 |2024-06-13 10:00:00|
+---+-----------+-------------------+
+---+-----------+-----------------------+
|key|total_value|max_event_time |
+---+-----------+-----------------------+
|1 |10 |2024-06-13 10:00:00 |
|0 |0 |2024-06-15 16:22:23.642|
|8 |8 |2024-06-15 16:20:23.642|
|2 |4 |2024-06-15 16:24:23.642|
|4 |8 |2024-06-15 16:26:23.642|
|9 |9 |2024-06-15 16:21:23.642|
|5 |5 |2024-06-15 16:17:23.642|
|1 |2 |2024-06-15 16:23:23.642|
|3 |6 |2024-06-15 16:25:23.642|
|6 |6 |2024-06-15 16:18:23.642|
|7 |7 |2024-06-15 16:19:23.642|
+---+-----------+-----------------------+
HTH
Mich Talebzadeh,
Technologist | Architect | Data Engineer | Generative AI | FinCrime
Disclaimer: The information provided is correct to the best of my knowledge
but of course cannot be guaranteed . It is essential to note that, as with
any advice, quote "one test result is worth one-thousand expert opinions
(Werner Von Braun)".
Mich Talebzadeh,
Technologist | Architect | Data Engineer | Generative AI | FinCrime
PhD <https://en.wikipedia.org/wiki/Doctor_of_Philosophy> Imperial College
London <https://en.wikipedia.org/wiki/Imperial_College_London>
London, United Kingdom
view my Linkedin profile
<https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
https://en.everybodywiki.com/Mich_Talebzadeh
*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner <https://en.wikipedia.org/wiki/Wernher_von_Braun>Von
Braun <https://en.wikipedia.org/wiki/Wernher_von_Braun>)".
On Fri, 14 Jun 2024 at 20:13, Om Prakash <[email protected]> wrote:
> Hi Team,
>
> Hope you all are doing well. I have run into a use case in which I want to
> do the aggregation in foreachbatch and use update mode for handling late
> data in structured streaming. Will this approach work in effectively
> capturing late arriving data in the aggregations? Please help.
>
>
>
> Thanking you,
> Om Prakash
>
