+1 On Wed, May 28, 2025 at 12:34 PM huaxin gao <huaxin.ga...@gmail.com> wrote:
> +1 > By unifying batch and low-latency streaming in Spark, we can eliminate the > need for separate streaming engines, reducing system complexity and > operational cost. Excited to see this direction! > > On Wed, May 28, 2025 at 9:08 AM Mich Talebzadeh <mich.talebza...@gmail.com> > wrote: > >> Hi, >> >> My point about "in real time application or data, there is nothing as an >> answer which is supposed to be late and correct. The timeliness is part of >> the application. if I get the right answer too slowly it becomes useless or >> wrong" is actually fundamental to *why* we need this Spark Structured >> Streaming proposal. >> >> The proposal is precisely about enabling Spark to power applications >> where, as I define it, the *timeliness* of the answer is as critical as >> its *correctness*. Spark's current streaming engine, primarily operating >> on micro-batches, often delivers results that are technically "correct" but >> arrive too late to be truly useful for certain high-stakes, real-time >> scenarios. This makes them "useless or wrong" in a practical, >> business-critical sense. >> >> For example *in real-time fraud detection* and In *high-frequency >> trading,* market data or trade execution commands must be delivered with >> minimal latency. Even a slight delay can mean missed opportunities or >> significant financial losses, making a "correct" price update useless if >> it's not instantaneous. able for these demanding use cases, where a >> "late but correct" answer is simply not good enough. As a colliery it is a >> fundamental concept, so it has to be treated as such not as a comment.in >> SPIP >> >> Hope this clarifies the connection in practical terms >> Dr Mich Talebzadeh, >> Architect | Data Science | Financial Crime | Forensic Analysis | GDPR >> >> view my Linkedin profile >> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/> >> >> >> >> >> >> On Wed, 28 May 2025 at 16:32, Denny Lee <denny.g....@gmail.com> wrote: >> >>> Hey Mich, >>> >>> Sorry, I may be missing something here but what does your definition >>> here have to do with the SPIP? Perhaps add comments directly to the SPIP >>> to provide context as the code snippet below is a direct copy from the SPIP >>> itself. >>> >>> Thanks, >>> Denny >>> >>> >>> >>> >>> On Wed, May 28, 2025 at 06:48 Mich Talebzadeh <mich.talebza...@gmail.com> >>> wrote: >>> >>>> just to add >>>> >>>> A stronger definition of real time. The engineering definition of real >>>> time is roughly fast enough to be interactive >>>> >>>> However, I put a stronger definition. In real time application or data, >>>> there is nothing as an answer which is supposed to be late and correct. The >>>> timeliness is part of the application.if I get the right answer too slowly >>>> it becomes useless or wrong >>>> >>>> >>>> >>>> Dr Mich Talebzadeh, >>>> Architect | Data Science | Financial Crime | Forensic Analysis | GDPR >>>> >>>> view my Linkedin profile >>>> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/> >>>> >>>> >>>> >>>> >>>> >>>> On Wed, 28 May 2025 at 11:10, Mich Talebzadeh < >>>> mich.talebza...@gmail.com> wrote: >>>> >>>>> The current limitations in SSS come from micro-batching.If you are >>>>> going to reduce micro-batching, this reduction must be balanced against >>>>> the >>>>> available processing capacity of the cluster to prevent back pressure and >>>>> instability. In the case of Continuous Processing mode, a specific >>>>> continuous trigger with a desired checkpoint interval quote >>>>> >>>>> " >>>>> df.writeStream >>>>> .format("...") >>>>> .option("...") >>>>> .trigger(Trigger.RealTime(“300 Seconds”)) // new trigger type to >>>>> enable real-time Mode >>>>> .start() >>>>> This Trigger.RealTime signals that the query should run in the new >>>>> ultra low-latency execution mode. A time interval can also be specified, >>>>> e.g. “300 Seconds”, to indicate how long each micro-batch should run for. >>>>> " >>>>> >>>>> will inevitably depend on many factors. Not that simple >>>>> HTH >>>>> >>>>> >>>>> Dr Mich Talebzadeh, >>>>> Architect | Data Science | Financial Crime | Forensic Analysis | GDPR >>>>> >>>>> view my Linkedin profile >>>>> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/> >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> On Wed, 28 May 2025 at 05:13, Jerry Peng <jerry.boyang.p...@gmail.com> >>>>> wrote: >>>>> >>>>>> Hi all, >>>>>> >>>>>> I want to start a discussion thread for the SPIP titled “Real-Time >>>>>> Mode in Apache Spark Structured Streaming” that I've been working on with >>>>>> Siying Dong, Indrajit Roy, Chao Sun, Jungtaek Lim, and Michael Armbrust: >>>>>> [ >>>>>> JIRA <https://issues.apache.org/jira/browse/SPARK-52330>] [Doc >>>>>> <https://docs.google.com/document/d/1CvJvtlTGP6TwQIT4kW6GFT1JbdziAYOBvt60ybb7Dw8/edit?usp=sharing> >>>>>> ]. >>>>>> >>>>>> The SPIP proposes a new execution mode called “Real-time Mode” in >>>>>> Spark Structured Streaming that significantly lowers end-to-end latency >>>>>> for >>>>>> processing streams of data. >>>>>> >>>>>> A key principle of this proposal is compatibility. Our goal is to >>>>>> make Spark capable of handling streaming jobs that need results almost >>>>>> immediately (within O(100) milliseconds). We want to achieve this without >>>>>> changing the high-level DataFrame/Dataset API that users already use – so >>>>>> existing streaming queries can run in this new ultra-low-latency mode by >>>>>> simply turning it on, without rewriting their logic. >>>>>> >>>>>> In short, we’re trying to enable Spark to power real-time >>>>>> applications (like instant anomaly alerts or live personalization) that >>>>>> today cannot meet their latency requirements with Spark’s current >>>>>> streaming >>>>>> engine. >>>>>> >>>>>> We'd greatly appreciate your feedback, thoughts, and suggestions on >>>>>> this approach! >>>>>> >>>>>> -- Best, Yanbo
