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Author's profile photo Marcel Oenning

Comprehensive Guide to Parallel Processing in SAP Data Intelligence


Are you a pipeline developer working with SAP Data Intelligence? Is your custom Python operator the bottleneck of the overall pipeline execution? And you are you searching for more possibilities to parallelise the execution of pipeline operators aside from multi-instancing? – Then you found the right guide to solve your problems. This guide aims to provide a comprehensive overview of possible parallelisation methods in SAP Data Intelligence. It will help you to increase the throughput of your pipelines based on your needs. 

A data processing pipeline on Data Intelligence can typically be divided into three parts: 

  1. Ingestors – source operators generating or streaming data into the pipeline 
  1. Processors – operators processing the data 
  1. Sinks – operators writing the processed data to the target system 

Most of the time the bottleneck of a pipeline is the “processing” part. Furthermore a lot of pipelines require custom logic to be implemented for this part. Most users decide to go for a custom Python operator. Therefore I decided to provide you with an example written in Python. 

This guide requires some technical background, a basic understanding of Python and a good understanding of the Data Intelligence Modeler application. If you do have any questions, you can reach out to me in the comments section below. 

If you do need some material to get started developing your first custom operator on Data Intelligence, check out these articles: 

Introduction to the Example Graph  


Example Graph used in this Guide

During this guide we will use the following simple graph to illustrate on how we can optimise our graph throughput by parallelisation. The first operator is a standard “Constant Generator” operator configured to produce an output every 250ms. And the second operator is a custom Python operator processing the input within 1s. The processing part is emulated by sleeping 1s before sending an output to the 3rd operator. The third operator is waiting for 10 inputs before sending the measured performance (in time) to the wiretap. 

So overall we have a processing operator that is 4x slower than the source operator. This is our current bottleneck. The first operator can produce 10 Messages in 2.5s (each 250ms) but the second operator can only process 1 Message per second. This leads to back-pressure in the pipeline and a 4x performance loss with a total runtime of 10s for 10 Messages. 

You can copy the following JSON into your Data Intelligence Modeler application when creating a new graph (gen1) from scratch. Use this as a template to apply the parallelisation methods below. Same methods can be applied to gen2 graphs with small adjustments to the new API. 

Attention! All examples shown below can also be downloaded from this github repository and imported into your system. As an alternative you can copy the main graph below to adapt it yourself, following the guide step by step. 

{"properties":{},"description":"[Ex] Main Graph - Parallelisation Guide","processes":{"constantgenerator1":{"component":"","metadata":{"label":"250ms Generator","x":17,"y":12,"height":80,"width":120,"extensible":true,"generation":1,"config":{"mode":"pulse","duration":"250ms"}}},"python3operator1":{"component":"","metadata":{"label":"Capture Time (10)","x":355,"y":12,"height":80,"width":120,"extensible":true,"filesRequired":[""],"generation":1,"config":{"script":"from datetime import datetime\nlast =\nn_inputs = 0\n\ndef on_input(data):\n    global last\n    global n_inputs\n    n_inputs += 1\n    \n    if n_inputs == 10:\n        now =\n        diff = now - last\n        last = now\n        n_inputs = 0\n        api.send(\"out\", str(diff))\n\napi.set_port_callback(\"in\", on_input)"},"additionalinports":[{"name":"in","type":"string"}],"additionaloutports":[{"name":"out","type":"string"}]}},"wiretap1":{"component":"","metadata":{"label":"Wiretap","x":524,"y":12,"height":80,"width":120,"generation":1,"ui":"dynpath","config":{}}},"python3operator2":{"component":"","metadata":{"label":"1s ","x":186,"y":12,"height":80,"width":120,"extensible":true,"filesRequired":[""],"generation":1,"config":{"script":"import time\n\ndef on_input(message):\n    # Delay (process message in 1s)\n    time.sleep(1)\n    api.send(\"out\", message)\n    \napi.set_port_callback(\"in\", on_input)"},"additionalinports":[{"name":"in","type":"string"}],"additionaloutports":[{"name":"out","type":"string"}]}}},"groups":[],"connections":[{"metadata":{"points":"479,52 519,52"},"src":{"port":"out","process":"python3operator1"},"tgt":{"port":"in","process":"wiretap1"}},{"metadata":{"points":"141,52 181,52"},"src":{"port":"out","process":"constantgenerator1"},"tgt":{"port":"in","process":"python3operator2"}},{"metadata":{"points":"310,52 350,52"},"src":{"port":"out","process":"python3operator2"},"tgt":{"port":"in","process":"python3operator1"}}],"inports":{},"outports":{},"metadata":{"generation":1}}

Method 1: Multi-Instancing (Grouping / Multiplicity)


Method 1: Multi-instancing (Grouping)

The first option is the most well-known and also the easiest to apply. Simply group the affected operator(s) in the graph and configure the group’s multiplicity (number of parallel instances executing the underlying code). SAP Data Intelligence will then spawn as many pods in the Kubernetes cluster as you need. Therefore this approach is fully capable of leveraging the full Kubernetes capabilities by scheduling a bunch of pods across the cluster. One limitation of this functionality is, that it isn’t scaling up or down automatically. This can lead to a lot of idle resources, if the fluctuation in the throughput of the pipeline is high. Another disadvantage is that you will be loosing the order of the incoming Messages. So if you rely on the last Message to terminate your graph, you will either have to adapt the termination logic of your graph or you will have to use another parallelisation method shown below. 


  • Easy to apply 
  • Fully leverages the capabilities of Kubernetes by spawning multiple pods 


  • Inflexible (no auto-scaling). Note: Autoscaling is a requested feature that might be available in a later release. It wasn’t available when publishing this blog 
  • Possibility of idle resources due to missing autoscaling functionality 
  • Message order is lost
    -> Can be remedied by providing an index with each input initially and then writing a “gathering” operator that reorders the results after they have been sent out of the group

Steps to apply method 1 to the main graph: 

  1. Group the affected operator(s) 
  1. Configure the group’s multiplicity 

With a multiplicity of 1 your graph will need 10s for 10 Messages.
With a multiplicity of 2 – 5s, 3 – 3.33s and with a multiplicity of 4 – 2.5s.
A higher multiplicity than 4 still yields to a performance of 2.5s and just leads to idle resources in the cluster. Obviously, unless you optimise the producing operator as well. 

Method 2: Master-Worker Pattern 


Method 2: Multi Processing with a Master-Worker architecture

The next methods I am going to present, will be increasing the throughput of the “1s” operator with the help of multi-processing rather than multi-instancing. This means that the code of that operator will be executed in a single execution instance (1 Kubernetes pod/container), but within that single execution instance multiple processes will be spawned. This will already avoid most of the mentioned disadvantages of using the built-in multi-instancing approach. 

Below you can find the full code snippet needed to parallelise the operator using the master-worker design pattern. Every detail of the code will be explained here step by step. 

To make use of the multiprocessing library, we must import the parallelized function instead of defining it in the operator script directly (due to some architectural restrictions – Basically we are interpreting and running your code from a base operator and if you try to parallelise a function within the given script it will fail with a pickle error message). 

Therefore I created a second file in the repository of my Data Intelligence cluster at “subengines/com/sap/python36/operators/com/example/”. This file can then be imported using the following statement 

from import parallel_fun 

It is important that every folder in the path contains an empty (0 byte) “__init.py__” file. Also it is important that the file is stored at “subengines/com/sap/python36/operators” path and not in the “operators” directory. 

To allow a communication between the master and worker processes I have created an in_queue. The master will send any incoming messages directly into this queue and the workers will be consuming the messages from this queue. Additionally, I have created an out_queue which is used to send the results from the worker processes back to the master process. 

Afterwards we are spawning 4 workers (daemon processes instead of pod instances) that should be processing the incoming messages. The processes are defined as daemon processes because they should keep running in the background even if they are idle. 

The on_input callback contains the logic of the master process to forward any incoming message to the worker’s queue. Additionally, the master will periodically check the out_queue for possible results returned from the workers using a timer callback. If it receives a result, then this result will be sent to the operator’s output port. 

The workers will keep collecting the messages from the given in_queue until they receive a shutdown signal. You can see the shutdown logic in the shutdown_workers method below. 

import multiprocessing 
from multiprocessing import Pool, get_context 
from import parallel_fun 

q_in = multiprocessing.Queue(1) 
q_out = multiprocessing.Queue() 

# Spawn workers 
n_proc = 4 
proc = [multiprocessing.Process(target=parallel_fun, args=(q_in, q_out)) for _ in range(n_proc)] 
for p in proc: 
    p.daemon = True 

# Input callback sends data to in queue 
def on_input(message): 
    # Just put the Message into the queue for the workers 
    q_in.put((False, message)) 

# Timer callback is handling the results from the out queue 
import queue 
def t1(): 
        out = q_out.get() 
        api.send("out", out) 
    except queue.Empty: 
# "0" timer callback is started as quickly as possible (basically a while loop) 
# Increase time if you expect the out_queue to be empty most of the time 
api.add_timer("0", t1) 
# shutdown the workers 
def shutdown_workers(): 
    for _ in range(n_proc): 
        q_in.put((True, None)) 

api.set_port_callback("in", on_input) 

The targeted parallelised function of the workers can be found in the file and looks like this: 

import time 

def parallel_fun(q_in, q_out): 
    while True: 
        shutdown, x = q_in.get() 
        if shutdown is True: 


  • You can scale the CPU consumption of a single pod (execution instance) to your needs.
    So instead of having 10 pods consuming each 10x memory and 0.1 CPU
    You can have 1 pod consuming less memory in total and 1 CPU. 
  • Can provide you with auto-scaling: 
  • Upscaling in the on_input callback whenever a lot of messages arrive within a short time period or when the in_queue is blocked (using a timeout). -> Just spawn a new worker process and add it to the global list. 
  • Downscaling when no new input messages arrived on the on_input callback for a given time period. -> Just send a shutdown signal into the in_queue and one of the workers will shutdown. 


  • Message order is lost 
    -> Can be remedied by providing an index with each input initially and then writing a “gathering” operator that reorders the results after they have been sent out of the operator
  • Possible overhead of idle workers. 
  • Does not leverage the Kubernetes capabilities as all processes are running in the same pod, container on the same node in the cluster.
    -> Can be remedied by combining this method with method 1. 

Steps to apply method 2 to the main graph: 

  1. Copy and Paste the provided script into the “1s” operator editor. 
  1. Make sure to have parallel_fun3 available in the file “subengines/com/sap/python36/operators/com/example/” and make sure that all folders in that path contain an empty (0 byte) file called “__init.py__” 

With 4 workers the graph is yielding to the maximum performance of 2.5s per 10 Messages. Having less workers will result in back-pressure in the given pipeline because the “1s” operator is processing the inputs slower than how they are generated. Having more than 4 workers will result in additional idle processes because they have to wait for new messages on the empty input_queue. 

Method 3: Multi-Processing (1 Process for 1 Input) 


Method 3: Multi Processing by spawning processes for complex inputs

This next method looks very similar to method 2 but with one slight difference: It does spawn a new daemon process with every given input Message. And therefore does not need any in_queue, but rather passes the given input Message directly to the newly spawned process. 

You might be wondering when this slightly different pattern might be useful. The answer might be that you have a lot of small inputs that can be processed by the master process in a short time period, but only very few big inputs that take very long to be processed. In that case you can avoid the possible overhead of idle workers and just process all inputs in the master process directly and whenever you receive a big input you can pass it to a child process. 

If you had no parallelisation then the single big input will block the (sequential) processing of all following smaller messages. But if you had implemented the master-worker pattern then the workers might be idle whenever they only receive small inputs but too busy or even blocked when receiving only big inputs. 

from multiprocessing import Pool, get_context 
from import parallel_fun2 
import multiprocessing as mp 

q = mp.Queue() 
# Process 
def on_input(message): 
    # Only spawn the process here 
    # Dont get the data from the queue. Otherwhise your main process will be blocked again 
    # Also don't join daemon processes."logs before Parallel start") 
    p = mp.Process(target=parallel_fun2, args=(message, q)) 
    p.daemon = True 
    p.start()"logs after Parallel start") 

# Timer callback is handling the results from the out queue 
import queue 
def t1(): 
        result = q.get() 
        api.send("out", result) 
    except queue.Empty: 
# "0" timer callback is started as quickly as possible (basically a while loop) 
# Increase time if you expect the out_queue to be empty most of the time 
api.add_timer("0", t1) 

api.set_port_callback("in", on_input) 

Because of the missing in_queue the targeted parallelised function in the file is slightly different. Also the daemon process doesn’t need any shutdown signal as it terminates automatically when it has processed the given Message.: 

import time 

def parallel_fun2(message, q_out): 


  • Very flexible to fluctuating input processing complexities (“big” complex inputs vs “small” easy inputs) 
  • Auto-Scaling 


  • Message order is lost 
    -> Can be remedied by providing an index with each input initially and then writing a “gathering” operator that reorders the results after they have been sent out of the operator
  • “big” complex Messages are processed slowly in the background. 
  • Inflexible to many “small” easy inputs
    -> Can be remedied by combining this method with method 2. Basically having a Master-Worker pattern but filtering the “big” complex inputs and spawning separate processes for them. 
  • Does not leverage the Kubernetes capabilities as all processes are running in the same pod, container on the same node in the cluster.
    -> Can be remedied by combining this method with method 1. 

Steps to apply method 3 to the main graph: 

  1. Copy and Paste the provided script into the “1s” operator editor. 
  1. Make sure to have parallel_fun3 available in the file “subengines/com/sap/python36/operators/com/example/” and make sure that all folders in that path contain an empty (0 byte) file called “__init.py__” 

Method 4: Batched Multi-Processing (n Processes per Input) 


Method 4: Multi Processing using Process Pools

As we have learned with method 3 we can already avoid a lot of disadvantages from method 1 and 2 while still keeping their advantages. But we still have the potential to optimise the processing of these “big” complex inputs. In some cases we can avoid them completely by dividing them into smaller chunks. 

These smaller chunks can then be treated as singular smaller inputs and sending them to the worker processes as we did in method 2. However, often this is not possible because the pipeline might require that the given input should result into a single output. 

In these cases where you have a “big” complex input, which can be divided in smaller chunks (in other words can be processed in parallel) but, on the other hand, needs to yield into a single result send to the output port. In those cases we can make use of so-called Process Pools. 

To simulate such bigger inputs I have adapted the given example graph by collecting 10 inputs in a separate Python operator and then sending this big Message to the “1s” operator. 

messages = [] 

def on_input(message): 
    global messages 
    # Collect messages in a list 

    # Until we have 10 and send them out as a batch of 10 messages 
    if len(messages) == 10: 
        api.send("batch", messages.copy()) 
        # Reset the variable for the next batch 
        messages = [] 

api.set_port_callback("in", on_input) 

Let’s assume this operator would typically need 1s for smaller Messages, but in this case the input is so big that it would require 10s of processing time. To avoid such long processing times, we are spawning a process pool of size 10 and provide the list of 10 Messages to pool. The get_context(“spawn”) statement is needed due to the architectural restrictions mentioned above. Once we retrieved all results we could send them to the next operator in a single Message or split them into smaller output Messages again as shown below. 

from multiprocessing import Pool, get_context 
from import parallel_fun3 

# Pool 
def on_input(data): 
    # the list of messages is in the input data's body 
    messages = data.body"logs before Parallel start") 
    # Create a pool with 10 processes 
    with get_context("spawn").Pool(10) as pool: 
        # Run parallel function and split the input among them 
        results =, data.body) 
        # Send out the results as single messages 
        for result in results: 
            api.send("out", result)"logs after Parallel start") 

api.set_port_callback("batch", on_input)


In this example we are not using any queues but rather return the results in the targeted parallelised function directly. It is also located in the file 

import time

def parallel_fun3(message): 
    return message 



  • Message order is kept
  • Divide and Conquer approach results in good performance for “big” complex inputs 


  • Can only be used when you have:
    – “big” complex inputs
    – that can be chunked and processed in parallel
    – and ideally all chunks require the same processing time (don’t differ in processing complexity) 
  • Does not leverage the Kubernetes capabilities as all processes are running in the same pod, container on the same node in the cluster.
    -> Can be remedied by sending the chunks to another operator using method 1. 

Steps to apply method 4 to the main graph: 

  1. Create a “collect” operator with a Basic Type “string” input called ‘in’ and an output of Basic Type “message” called ‘batch’. 
  1. Adapt the “1s” operator input port by changing it’s type to ‘message’ as well and the name to ‘batch’. 
  1. Copy and Paste the first provided script into the “collect” operator 
  1. Copy and Paste the second provided script into the “1s” operator editor. 
  1. Make sure to have parallel_fun3 available in the file “subengines/com/sap/python36/operators/com/example/” and make sure that all folders in that path contain an empty (0 byte) file called “__init.py__” 

Summary and “When to Use What”? 

In this guide you should have learned how SAP Data Intelligence can be used to scale your data processing pipelines up and down to your needs. And that with small efforts you can even create auto-scaling processing operators. The “multi-instancing” (grouping) functionality is an easy to use built-in functionality, which helps to scale pipelines across several pods running on different nodes in your cluster. Whenever this feature is not fulfilling your requirements, you can make use of the standard multiprocessing Python library to finetune your data pipeline. In most cases a combination of the presented methods will yield into the best performance. To get an idea of these possible combinations, I have created an overview of possible situations and which method(s) can then be used to optimize your pipeline throughput. 


Scenario (multiple can apply to you) Method to Use (additionally) 
You want to leverage Kubernetes capabilities and distribute workload across different pods in your cluster  (1) 
You want to parallelise within the pods running in your cluster  (2) and/or (3) and/or (4) 
You have consistent inputs with comparable complexity  (1) and/or (2) 
Only some of your total inputs are complex and block your (sequential) pipeline process  (3) or (3)+(2) or (3)+(2)+(1) 
You have complex inputs which can be processed in smaller chunks but need to yield into a result that can be merged   (4) 


Enjoyed reading this guide? Any other patterns you want me to explore? – Let me know in the comments section below. You can also post a question related to SAP Data Intelligence here. Also make sure to checkout one of my other blog articles: 

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      Author's profile photo Werner Dähn
      Werner Dähn

      So only first option is native to DI, all other options are done in the python code and therefore it applies to all tools, obviously?

      Would be nice if you could write a blog about end-to-end parallel processing also. I believe this is more relevant in practice.

      For example, if the source can be read partitioned (partitioned database table, CSV file split into equal sized parts, Parquet directory structure, 4x 1s Generators in your example,...) then you could create a pipeline that has n parallel streams, end to end. This is how all high throughput pipeline tool work.


      If the load is partition-aware as well and the partitions are not in sync with the source, a reshuffle is required upfront the load. This variant would be a pipeline like:


      The advantage of the end to end partitioning is that

      • Order is preserved (within each partition)
      • Autoscaling could be used (100 partitions are processed with up to 20 parallel pipeline instances; as soon as one partition is finished it picks the next waiting one)
      • no idle resources
      • simple


      • source must be partitioned

      If the source cannot be partitioned, then at least everything later can be processed in parallel, similar to such a pipeline:

      Author's profile photo Marcel Oenning
      Marcel Oenning
      Blog Post Author

      Yes, currently only the first option is SAP Data Intelligence native and the others can also be established with other tools using Python code. However, there are also some standard operators which do expose configurations to make use of multi-processing (e.g. the openAPI ServFlow operator). But this option is not exposed for all standard operators.

      The main purpose of this guide is to provide an overview of the different possibilities to scale specific parts of your data pipeline. The focus is often on custom operators, so I also included the options that require some Python coding. Some readers might have tried to establish a multi-processing in SAP Data Intelligence and failed because of the architectural restrictions mentioned in the chapter of method 2 (master-worker multi-processing).

      Your first given example graph can be realised in Data Intelligence with both multi-processing and the built-in grouping - method 1 (multi-instancing). The only difference is the impact on the infrastructure. Do you want your workload to be distributed across the cluster and be processed in independent instances (running containers)? Or do you rather want to parallelise within a single execution instance?

      There is no single answer to that question. Multi-processing can lead to single high consuming instances that reach the limitation of a single node in your cluster. Multi-instancing can lead to a lot of overhead by scheduling a lot of different pods (which all require a unique IP address and sometimes are even limited by the hyperscalers). E.g. Microsoft Azure per default only allows to schedule 110 pods per nodes. This might seem to be a lot, but if you are scheduling a lot of very small pods and if you have rather big nodes then you might reach this limitation without consuming all the available resources of that node. From my personal experience a combination of multi-instancing and multi-processing often gets the job done as you can adjust the size of the single execution instances to your need using multi-processing and then scale further using the built-in multi-instancing approach.

      Regarding your partitioning approach: Currently this is not possible using standard operators. I do know that there is an open feature request for that. Until then, you could also achieve this goal both in gen1 and gen2 using some Python coding (to partition and reshuffle). And combine it with the presented methods for the auto-scaling functionality. Personally however I am optimistic that partitioning (especially for Parquet) will be part of the standard delivery at least for gen2 operators. I would recommend you to push your request via the "customer influence page" channel here. On this page you can create feature requests and support the requests of other customers and have a direct influence to our roadmap.

      Edit: I will be implementing your suggested pattern and update the blog accordingly so others can use it as an example on how to parallelise on different partitions (and within those partitions you can use the other methods mentioned above).

      Author's profile photo Lochner Louw
      Lochner Louw

      Hey Marcel,

      Love the examples in the post.

      To build on what Werner Dähn said.

      I have a similar approach to fix the order issue in "option 1" where you use multiplicity of the groups. If you add message.index, batchTotal, lastBatch to the message attributes/header from the generator then you can keep track of the order somewhat when you put an aggregator that is batch aware after the group multiplicity. It is like a pseudo scatter-gather pattern at the end of the day.



      Author's profile photo Marcel Oenning
      Marcel Oenning
      Blog Post Author

      Hey Lochner,

      thank you very much for your suggestion. I have added it to the blog!

      Have a nice day. Best Regards


      Author's profile photo BIBEK PATRO

      HI, I see the instructions given in different methods are not consistent. for example, __init.py__ and one is correct?


      ALso i have tried using method 3, but the child function parallel_fun2 is not getting invoked and not sure what is missing here.

      Also regrding, where I need to place and can you please show me any example? This statement is confusing.

      1. Make sure to have parallel_fun3 available in the file “subengines/com/sap/python36/operators/com/example/” and make sure that all folders in that path contain an empty (0 byte) file called “__init.py__”

      Can you please guide me here?



      Author's profile photo Marcel Oenning
      Marcel Oenning
      Blog Post Author

      Hi Bibek,

      sure let me guide you through.

      First of all thanks for your comment. The correct naming of the python 0 byte files is "".

      The reason why the parallel_fun2 is not invoked is probably because your file is in the wrong location or "" files are missing in the route to that direction.

      The simplest explanation that I can think of is to refer you to the github repository here.
      In this repository you can find all examples and also the correct location of the in the Data Intelligence repository.

      In the example I only created a new subfolder "example". The other directories in the path to are already part of the standard delivery and therefore already contain those empty init files.

      As you can see I have created the and the files in that example folder.

      The pycache folder is generated at runtime when you execute the graph.

      Hope this helps. Have a nice day.

      Kind Regards


      Author's profile photo BIBEK PATRO

      HI Marcel,

      Big thanks for providing detailed clarifications.

      As you mentioned, I have kept my file exactly as per your example path. Please find the path below:



      APart from this, I have also kept (a 0 byte file) in all the folders in the above path like subengines, com, sap. python36, operators, com, example since I didnt find init file as part of standard delivery.

      my scenario is like below:

      1. my source operator is ABAP ODP reader which sends data in batches/packets.
      2. for each packet DI receive, i am using your code to create a process and pass the incming messages to
      3. would read message and convert to dataframe. then this df would be converted to CSV post transformation and will be added q_out.
      4. now in my main program, i have timer which constantly reads the q and would pass the result to next operator.

      the issue I am facing is is not reachable as I feel and nothing is getting added to q when I am reading. Do I need to add init file files and vflow folder as well ?

      also I read somewhere that i need to define main program before calling the daemon process. for example, is it required?

      if __name__ == "__main__":"logs before Parallel start")
      p = mp.Process(target=parallel_fun, args=(data, q))
      p.daemon = True
      api.send("out",str("logs after Parallel start")

      Is there anything that I am missing still. I have followed your blog closely and replicated all steps as you have mentioned in method 3.

      Thanks in advance for your further guidance.



      Author's profile photo Ankit Sharma
      Ankit Sharma

      Hi Bibek,

      I also have similar kind of scenario and facing similar issue i.e. parallel_fun2 is not getting invoked . I tried all the steps mentioned in blog.


      Are you able to resolve it?.


      Please let me know if you have any information.




      Author's profile photo Leena Gopinath
      Leena Gopinath

      Thanks Marcel for the blog! Marcel Oenning

      But, Will this grouping and parallel processing work for the V1 / V2 operators and the subscriptions?

      The graph fails with the subscription mechanism, which does not allow to create 2 subscriptions with the same name.

      What is the solution on this regard then -- if you would work around this you can extract 2 times the same data with 2 different subscription names.

      Any thoughts on this?

      Best Regards


      Author's profile photo Michael Cocquerel
      Michael Cocquerel

      I have tried to add"some text") in the parallel_fun function in the method 2 example:

      import time 
      def parallel_fun(q_in, q_out): 
          while True: 
              shutdown, x = q_in.get() 
              if shutdown is True: 
    "some text")

      After doing that, the Wiretab does not display messages anymore. I do not see any error in the log, even in DEBUG mode.

      Does that mean, it is not supported to use api.logger in the worker thread ? and that any error in worker thread does not appear in the log ?

      Is there any method to propagate the worker error message to the master ?