kandi X-RAY | eventpy Summary
kandi X-RAY | eventpy Summary
eventpy is a Python event library that provides tools that enable your application components to communicate with each other by dispatching events and listening for them. With eventpy you can easily implement signal/slot mechanism, or observer pattern. This library is the Python version rewritten from the C++ library eventpp, both are developed by the same developer.
Top functions reviewed by kandi - BETA
- Dispatches an event
- Returns the callable list for the given event
- Dispatch an event
- Get the event from args
eventpy Key Features
eventpy Examples and Code Snippets
Trending Discussions on Pub Sub
In R, I want to build json content according this Google Cloud Pub Sub message format: https://cloud.google.com/pubsub/docs/reference/rest/v1/PubsubMessage
It have to respect :...
ANSWERAnswered 2022-Apr-16 at 09:59
Not sure why, but replacing the dataframe by a list seems to work:
My basic requirement was to create a pipeline to read from BigQuery Table and then convert it into JSON and pass it onto a PubSub topic.
At first I read from Big Query and tried to write it into Pub Sub Topic but got an
exception error saying "Pub Sub" is not supported for batch pipelines. So I tried some workarounds and
I was able to work around this in python by
- Reading from BigQuery-> ConvertTo JSON string-> Save as text file in cloud storage (Beam pipeline)
ANSWERAnswered 2021-Oct-14 at 20:27
Because your pipeline does not have any unbounded PCollections, it will be automatically run in batch mode. You can force a pipeline to run in streaming mode with the
--streaming command line flag.
We are using Pub Sub lite instances along with reservations, we want to deploy it via Terraform, on UI while creating a Pub Sub Lite we get an option to specify Peak Publish Throughput (MiB/s) and Peak Subscribe Throughput (MiB/s) which is not available in the resource "google_pubsub_lite_topic" as per this doc https://registry.terraform.io/providers/hashicorp/google/latest/docs/resources/pubsub_lite_topic.
ANSWERAnswered 2022-Feb-20 at 21:46
If you check the bottom of your Google Cloud console screenshot, you can see it suggests to have 4 partitions with 4MiB/s publish and subscribe throughput.
Therefore your Terraform
partition_config should match this. Count should be 4 for the 4 partitions, with capacity of 4MiB/s publish and 4MiB/s subscribe for each partition.
The "peak throughput" in web UI is just for convenience to help you choose some numbers here. The actual underlying PubSub Lite API doesn't actually have this field, which is why there is no Terraform setting either. You will notice the sample docs require a per-partiton setting just like Terraform.
I think the only other alternative would be to create a
reservation attached to your topic with enough throughput units for desired capacity. And then completely omit
capacity block in Terraform and let the reservation decide.
I have a User that needs to be able to query and create Jetstream keyvalue stores. I attempted to add pub/sub access to $JS.API.STREAM.INFO.* in order to give the User the ability to query and create keyvalue stores:...
ANSWERAnswered 2022-Jan-31 at 16:16
nsc edit user RequestCacheService --allow-pubsub '$JS.API.STREAM.INFO.*'
With single-quotes around the subject. I was under the impression that double & single quotes would escape the
$ but apparently only single-quote will escape special characters in the subject.
I am deciding if I should use MSK (managed kafka from AWS) or a combination of SQS + SNS to achieve a pub sub model?
Currently, we have a micro service architecture but we don't use any messaging service and only use REST apis (dont ask why - related to some 3rd party vendors who designed the architecture). Now, I want to revamp it and start using messaging for communication between micro-services.
Initially, the plan is to start publishing entity events for any other micro service to consume - these events will also be stored in data lake in S3 which will also serve as a base for starting data team.
Later, I want to move certain features from REST to async communication.
Anyway, the main question I have is - should I decide to go with MSK or should I use SQS + SNS for the same? ( I already understand the basic concepts but wanted to understand from fellow community if there are some other pros and cons)?
Thanks in advance...
ANSWERAnswered 2022-Feb-09 at 17:58
MSK VS SQS+SNS is not really 1:1 comparison. The choice depends on various use cases. Please find out some of specific difference between two
- Scalability -> MSK has better scalability option because of inherent design of partitions that allow parallelism and ordering of message. SNS has limitation of 300 publish/Second, to achieve same performance as MSK, there need to have higher number of SNS topic for same purpose.
Example : Topic: Order Service in MSK -> one topic+ 10 Partitions SNS -> 10 topics
if client/message producer use 10 SNS topic for same purpose, then client needs to have information of all 10 SNS topic and distribution of message. In MSK, it's pretty straightforward, key needs to send in message and kafka will allocate the partition based on Key value.
Administration/Operation -> SNS+SQS setup is much simpler compare to MSK. Operational challenge is much more with MSK( even this is managed service). MSK needs more in depth skills to use optimally.
SNS +SQS VS SQS -> I believe you have multiple subscription(fanout) for same message thats why you have refer SNS +SQS. If you have One Subscription for one message, then only SQS is also sufficient.
Replay of message -> MSK can be use for replaying the already processed message. It will be tricky for SQS, though can be achieve by having duplicate queue so that can be use for replay.
After following the dataflow tutorial, I used the pub/sub topic to big query template to parse a JSON record into a table. The Job has been streaming for 21 days. During that time I have ingested about 5000 JSON records, containing 4 fields (around 250 bytes).
After the bill came this month I started to look into resource usage. I have used 2,017.52 vCPU hr, memory 7,565.825 GB hr, Total HDD 620,407.918 GB hr.
This seems absurdly high for the tiny amount of data I have been ingesting. Is there a minimum amount of data I should have before using dataflow? It seems over powered for small cases. Is there another preferred method for ingesting data from a pub sub topic? Is there a different configuration when setting up a Dataflow Job that uses less resources?...
ANSWERAnswered 2022-Feb-03 at 21:43
It seems that the numbers you mentioned, correspond to not customizing the job resources. By default streaming jobs use a n1-standar-4 machine:
3 Streaming worker defaults: 4 vCPU, 15 GB memory, 400 GB Persistent Disk.
4 vCPU x 24 hrs x 21 days = 2,016
15 GB x 24 hrs x 21 days = 7,560
If you really need streaming in Dataflow, you will need to pay for resources allocated even if there is nothing to process.
- Considering that the number and size of the JSON string you need to process are really small, you can reduce the cost to aprox 1/4 of current charge. You just need to set the job to use a n1-standard-1 machine, which has 1vCPU and 3.75GB memory. Just be careful with max nodes, unless you are planning increase the load, one node may be enough.
Your own way
- If you don't really need streaming (not likely), you can just create a function that pulls using Synchronous Pull, and add the part that writes to BigQuery. You can schedule according to your needs.
Cloud functions (my recommendation)
- You can create a serverless Event-Driven Cloud Function with a Cloud Pub/Sub trigger. This way, considering your low volume, you can take advantage of the Free Tier and keep the real time processing:
"Cloud Functions provides a perpetual free tier for compute-time resources, which includes an allocation of both GB-seconds and GHz-seconds. In addition to the 2 million invocations, the free tier provides 400,000 GB-seconds, 200,000 GHz-seconds of compute time and 5GB of Internet egress traffic per month."
I need to have a TCP client that listens to messages constantly (and publish pub sub events for each message)
Since there is no Kafka in GCP, I'm trying to do it using my flask service (which runs using AppEngine in GCP).
I'm planning on setting the
ANSWERAnswered 2021-Dec-27 at 16:07
I eventually went for implementing a Kafka connector myself and using Kafka.
Trigger a function which updates Cloud Firestore when a student completes assignments or assignments are added for any course.Problem
The official docs state that a feed for
CourseWorkChangesInfo requires a
courseId, and I would like to avoid having a registration and subscription for each course, each running on its own thread.
I have managed to get a registration to one course working:...
ANSWERAnswered 2021-Dec-22 at 08:48
This is not possible.
You cannot have a single registration to track course work changes for multiple courses, as you can see here:
File a feature request:
Types of feeds
The Classroom API currently offers three types of feed:
- Each domain has a roster changes for domain feed, which exposes notifications when students and teachers join and leave courses in that domain.
- Each course has a roster changes for course feed, which exposes notifications when students and teachers join and leave courses in that course.
- Each course has a course work changes for course feed, which exposes notifications when any course work or student submission objects are created or modified in that course.
If you think this feature could be useful, I'd suggest you to file a feature request in Issue Tracker using this template.Reference:
I've a method X that's getting data from the server via pub sub. This method returns a flow. I've another method that subscribes to the flow by method X but only wants to take the first 3 values max from the flow if the data is distinct compared to previous data. I've written the following code...
ANSWERAnswered 2021-Dec-17 at 19:13
You have a
Flow> here, which means every element of this flow is itself a list.
take operator is applied on the flow, so you will take the 3 first lists of the flow. Each individual list is not limited, unless you use
take on the list itself.
So the name
transformedListOf3Elements is incorrect, because the list is of an unknown number of elements, unless you filter it somehow in the
I am working with a Java API from a data vendor providing real time streams. I would like to process this stream using Akka streams.
The Java API has a pub sub design and roughly works like this:...
ANSWERAnswered 2021-Oct-26 at 13:31
To feed a
Source, you don't necessarily need to use a custom graph stage.
Source.queue will materialize as a buffered queue to which you can add elements which will then propagate through the stream.
There are a couple of tricky things to be aware of. The first is that there's some subtlety around materializing the
Source.queue so you can set up the subscription. Something like this:
No vulnerabilities reported
You can use eventpy like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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