OK,
This is a common issue in Spark Structured Streaming (SSS), where the
source generates data faster than Spark can process it. SSS doesn't have a
built-in mechanism for directly rate-limiting the incoming data stream
itself. However, consider the following:
- Limit the rate at which data is produced. This can involve configuring
the data source itself to emit data at a controlled rate or implementing
rate limiting mechanisms in the application or system that produces the
data.
- SSS supports backpressure, which allows it to dynamically adjust the
ingestion rate based on the processing capacity of the system. This can
help prevent overwhelming the system with data. To enable backpressure, set
the appropriate configuration properties such as
spark.conf.set("spark.streaming.backpressure.enabled",
"true") and spark.streaming.backpressure.initialRate.
- Consider adjusting the micro-batch interval to control the rate at
which data is processed. Increasing the micro-batch interval and reduce the
frequency of processing, allowing more time for each batch to be processed
and reducing the likelihood of out-of-memory
errors.. spark.conf.set("spark.sql.streaming.trigger.interval", "<n>
seconds"
- Dynamic Resource Allocation (DRA), Not implemented yet. DRA will
automatically adjust allocated resources based on workload. This ensures
Spark has enough resources to process incoming data within the trigger
interval, preventing backlogs and potential OOM issues.
>From Spark UI, look at the streaming tab. There are various statistics
there. In general your Processing Time has to be less than your batch
interval. The scheduling Delay and Total Delay are additional indicator of
health.
HTH
Mich Talebzadeh,
Technologist | Solutions Architect | Data Engineer | Generative AI
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 Sun, 7 Apr 2024 at 15:11, Baran, Mert <mert.ba...@fau.de> wrote:
> Hi Spark community,
>
> I have a Spark Structured Streaming application that reads data from a
> socket source (implemented very similarly to the
> TextSocketMicroBatchStream). The issue is that the source can generate
> data faster than Spark can process it, eventually leading to an
> OutOfMemoryError when Spark runs out of memory trying to queue up all
> the pending data.
>
> I'm looking for advice on the most idiomatic/recommended way in Spark to
> rate-limit data ingestion to avoid overwhelming the system.
>
> Approaches I've considered:
>
> 1. Using a BlockingQueue with a fixed size to throttle the data.
> However, this requires careful tuning of the queue size. If too small,
> it limits throughput; if too large, you risk batches taking too long.
>
> 2. Fetching a limited number of records in the PartitionReader's next(),
> adding the records into a queue and checking if the queue is empty.
> However, I'm not sure if there is a built-in way to dynamically scale
> the number of records fetched (i.e., dynamically calculating the offset)
> based on the system load and capabilities.
>
> So in summary, what is the recommended way to dynamically rate-limit a
> streaming source to match Spark's processing capacity and avoid
> out-of-memory issues? Are there any best practices or configuration
> options I should look at?
> Any guidance would be much appreciated! Let me know if you need any
> other details.
>
> Thanks,
> Mert
>
>
> ---------------------------------------------------------------------
> To unsubscribe e-mail: user-unsubscr...@spark.apache.org
>
>
