kandi X-RAY | streampie Summary
kandi X-RAY | streampie Summary
Streampie is a tiny library for simple and parallel execution of job processing tasks. The project heavily draws both concepts and code from the awesome stream.py project by Anh Hai Trinh. However, it is a leaner, cleaner re-implementation with the addition of simple distributed computation.
Top functions reviewed by kandi - BETA
- Run the worker
- Deserialize data
- Fetch the job from the database
- Push data to the input queue
- Enqueue a new job
- Finalize the request
- Handles the job
- Serializes a Python object
- Start the worker threads
- Control the worker
- Start the connection
- Control the output queue
- Worker thread
- Iterate over the queue
- Start the worker processes
- Get the next response from the queue
streampie Key Features
streampie Examples and Code Snippets
Trending Discussions on Stream Processing
Redpanda seems easy to work with, but how would one process streams in real-time?
We have a few thousand IoT devices that send us data every second. We would like to get the running average of the data from the last hour for each of the devices. Can the built-in WebAssembly stuff be used for this, or do we need something like Materialize?...
ANSWERAnswered 2022-Mar-28 at 16:19
Any Kafka library should work with RedPanda, including Kafka Streams, KSQL, Apache Spark, Flink, Storm, etc.
We have an Apache Flink application which processes events
- The application uses event time characteristics
- The application shards (
keyBy) events based on the
- The application has windowing with 1 minute tumbling window
- The windowing is specified by a
- So, for each session we will have 1 computed record
- The windowing is specified by a
- The application emits the data into a Postgres sink
- It is hosted in AWS via Kinesis Data Analytics (KDA)
- It is running in 5 different regions
- The exact same code is running in each region
- It is hosted in AWS via RDS (currently it is a PostgreSQL)
- It is located in one region (with a read replica in a different region)
Because we are using event time characteristics with 1 minute tumbling window all regions' sink emit their records nearly at the same time.
What we want to achieve is to add artificial delay between window and sink operators to postpone sink emition.
We have thought that we can add some sleep to evictor's
evictBefore like this
ANSWERAnswered 2022-Mar-07 at 16:03
You could use
TumblingEventTimeWindows of(Time size, Time offset, WindowStagger windowStagger) with
I would expect windows to trigger only after we wait until the maximum possible time as defined by the max lateness for watermark.
.assignTimestampsAndWatermarks( WatermarkStrategy.forBoundedOutOfOrderness(Duration.ofMillis(10000000)) .withTimestampAssigner((order, timestamp) -> order.getQuoteDatetime().getTime())) .keyBy(order-> GroupingsKey.builder().symbol(order.getSymbol()).expiration(order.getExpiration()) .build()) .window(EventTimeSessionWindows.withGap(Time.milliseconds(100000000)))
In this example, why would the window ever trigger in any meaningful amount of time? The window is a very large window and we wait a very long time for records. When I run my example, the window still gets triggered in under a minute. why is that?...
ANSWERAnswered 2021-Jul-25 at 04:48
Turns out the watermark was being generated after the source was exhausted(in this case it was from reading a file). So the max watermark was emitted(9223372036854775807).
A trigger happens when:
window.maxTimestamp() <= ctx.getCurrentWatermark()
Given a simple Apache Storm Topology that makes use of the Stream API, there are two ways of initializing an Stream:
Version 1 - implicit declaration...
ANSWERAnswered 2021-May-28 at 09:47
integerStream.filter(x -> x > 5); returns a new stream that you ignore.
All filtering examples in apache flink documentation display simple cases of filtering according to a global threshold.
But what if filtering on an entry should take into account the previous entry?
Let's say we have a stream of sensor data. We need to discard the current sensor data entry if it's X% larger than then previous entry.
Is there a simple solution for this? Either in Apache Flink or in plain Java.
ANSWERAnswered 2021-Apr-30 at 08:38
In flink, this can be done with state.
Your use case is very similar to the fraud detection example from flink doc.
I have a pipeline like this:...
ANSWERAnswered 2021-Apr-26 at 12:38
The pipeline you've described doesn't use any keyed state that would benefit from setting state TTL. The only keyed state in your pipeline is the contents of the session windows, and that state is being purged as soon as possible -- as the sessions close. (Furthermore, since you are using a reduce function, that state consists of just one value per key.)
For the most part, expiring state is only relevant for state you explicitly create, in which case you will have ready access to the state descriptor and can configure it to use State TTL. Flink SQL does create state on your behalf that might not automatically expire, in which case you will need to use Idle State Retention Time to configure it. The CEP library also creates state on your behalf, and in this case you should ensure that your patterns either eventually match or timeout.
While Apache Storm offers several metric types, I am interested in the Topology Metrics, (and not the Cluster Metrics or the Metrics v2. For these, a consumer has to be registered, for example as:...
ANSWERAnswered 2021-Apr-26 at 12:06
After looking at the right place, I found the related configuration:
topology.builtin.metrics.bucket.size.secs: 10 is they way to specify that interval in
Apache Flink is distributes its operators on available, free slots on the JobManagers (Slaves). As stated in the documentation, there is the possibility to set the
SlotSharingGroup for every operator contained in an execution. This means, that two operators can share the same slot, where they are executed later.
Unfortunately, this option only allows to share the same group but not to assign a streaming operation to a specific slot.
So my question is: What would be the best (or at least one) way to manually assign streaming operators to specific slots/workers in Apache Flink?...
ANSWERAnswered 2021-Mar-17 at 17:08
You could disable the chaining via (
disableChaining()) and start a new chain to isolate it from others via (
startNewChain()). You can play with Flink Plan Visualizer to see if your plan has isolated operators. These modifiers applied affter the operator. Example:
To understand what is
kafka-streams I should know what is
stream-processing. When I start reading about them online I am not able to grasp an overall picture, because it is a never ending tree of links to new concepts.
Can any one explain what is
stream-processing with a simple real-world example?
And how to relate it to
kafka-streams with producer consumer architecture?
ANSWERAnswered 2021-Feb-05 at 10:38
Stream Processing is based on the fundamental concept of unbounded streams of events (in contrast to static sets of bounded data as we typically find in relational databases).
Taking that unbounded stream of events, we often want to do something with it. An unbounded stream of events could be temperature readings from a sensor, network data from a router, order from an e-commerce system, and so on.
Let's imagine we want to take this unbounded stream of events, perhaps its manufacturing events from a factory about 'widgets' being manufactured.
We want to filter that stream based on a characteristic of the 'widget', and if it's
red route it to another stream. Maybe that stream we'll use for reporting, or driving another application that needs to respond to only
red widgets events:
This, in a rather crude nutshell, is stream processing. Stream processing is used to do things like:
- filter streams
- aggregate (for example, the sum of a field over a period of time, or a count of events in a given window)
- enrichment (deriving values within a stream of a events, or joining out to another stream)
As you mentioned, there are a large number of articles about this; without wanting to give you yet another link to follow, I would recommend this one.Kafka Streams
Kafka Streams a stream processing library, provided as part of Apache Kafka. You use it in your Java applications to do stream processing.
In the context of the above example it looks like this:
Kafka Streams is built on top of the Kafka producer/consumer API, and abstracts away some of the low-level complexities. You can learn more about it in the documentation.
To test out stream processing and Flink, I have given myself a seemingly simple problem. My Data stream consists of
y coordinates for a particle along with time
t at which the position was recorded. My objective is to annotate this data with velocity of the particular particle. So the stream might look some thing like this.
ANSWERAnswered 2021-Jan-31 at 17:07
One way of doing this in Flink might be to use a KeyedProcessFunction, i.e. a function that can:
- process each event in your stream
- maintain some state
- trigger some logic with a timer based on event time
So it would go something like this:
- you need to know some kind of "max out of orderness" about your data. Based on your description, let's assume 100ms for example, such that when processing data at timestamp
1612103771212you decide to consider you're sure to have received all data until
- your first step is to
keyBy()your stream, keying by particle id. This means that the logic of next operators in your Flink application can now be expressed in terms of a sequence of events of just one particle, and each particle is processed in this manner in parallel.
Something like this:
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