kestra | infinitely scalable orchestration and scheduling platform | BPM library
kandi X-RAY | kestra Summary
kandi X-RAY | kestra Summary
Kestra is an infinitely scalable orchestration and scheduling platform, creating, running, scheduling, and monitoring millions of complex pipelines.
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Top functions reviewed by kandi - BETA
- Process execution queue
- Gets the key for the given object
- Lock an execution
- Emit execution from executor
- Executes an image
- Renders the docker client
- Get the metadata from the run context
- Prepare topology
- Remove executor from stream
- Generate the documentation
- The Jackson ObjectMapper
- Returns the list of all token parsers
- Transform executor value
- Returns daily execution statistics for a flow
- Handle loop
- Stops the worker thread
- Provides the default filters
- Handles a producer record
- Serializes the logging event to JSON
- Process a Kafka consumer
- Get health information
- Executes a worker task
- Creates metadata
- Evaluates execution
- Applies the filter
- Executes the given abstract bash
kestra Key Features
kestra Examples and Code Snippets
public DateTime Timestamp { get; }
public class LongToDateConverter : IValueConverter
{
public object Convert(object value, Type targetType, object parameter, CultureInfo culture)
{
if (!(value is long
SET @after := '2022-04-04',
@before := '2022-04-06';
SELECT *
FROM `presences` AS p
JOIN (SELECT * FROM `days` WHERE `day` BETWEEN @after AND @before)
AS d ON d.`day` BETWEEN Date(p.`start`) AND Date(p.`end`) -- The core
#version 400 core
in vec2 position;
out vec2 pos;
void main(void)
{
pos=position;
gl_Position = vec4(position.xy,0.0,1.0);
}
#version 400 core
in vec2 pos;
out vec3 out_Color;
// light
const flo
n = 4 # number of cores
m = 1 # number of threads per core
TOTAL_MEMORY = 16282416 kB
TOTAL_MEMORY * min(1, 1 / 4)
> 4070604
lscpu --all --extended
➜ lscpu --all --extended
CPU NODE SOCKET CORE L1d:L1i:L2:L3 ONLINE MAXMHZ MINMHZ
0 0 0 0 0:0:0:0 yes 6700.0000 800.0000
1 0 0 0 0:0:0:0 yes 67
@Echo Off
SetLocal EnableExtensions DisableDelayedExpansion
Set /A "SKU=OSV=0"
For /F "EOL=O Tokens=1,2 Delims=. " %%G In ('%SystemRoot%\System32\wbem\WMIC.exe
OS Where "Version>10" Get OperatingSystemSKU^, Version 2^>NUL'
) Do Set
QT += core gui
greaterThan(QT_MAJOR_VERSION, 4): QT += widgets
CONFIG += c++14
# You can make your code fail to compile if it uses deprecated APIs.
# In order to do so, uncomment the following line.
#DEFINES += QT_DISABLE_DEPRECAT
dotnet new -i "Microsoft.Quantum.ProjectTemplates::0.2-*"
dotnet new -i "Microsoft.Quantum.ProjectTemplates"
dotnet new --update-apply
# Install the .NET Framework
For handlers to be fully non-blocking, you need to use reactive libraries throughout the processing chain,
all the way to the persistence layer.
By all means, keep using Spring MVC if you are developing web apps th
# Netcore 6
export PATH=$PATH:$HOME/.dotnet/tools
export DOTNET_ROOT=$HOME/.dotnet
export PATH=$PATH:$DOTNET_ROOT
❯ dotnet ef
_/\__
---==/ \\
___ ___
Community Discussions
Trending Discussions on kestra
QUESTION
I've a complex Kafka Stream application with 2 flows fully stateful in the same stream :
- it use a
Execution
topic as source, enhanced the message and republished back to the sameExecution
topic. - it join another topic
WorkerTaskResult
, add the result toExecution
and published back toExecution
Topic.
The main goal is to provide a workflow system.
The detailled logic are :
- an Execution is a list of TaskRun
- the
Execution
look at all the current state of allTaskRun
and find the next one to execute - If any is found, Execution alter their
TaskRunsList
and add the next one and publish back to Kafka, also it send to another queue the task to be done (WorkerTask
) - the
WorkerTask
is proceed outside of the Kafka stream and publish back to another queue (WorkerTaskResult
) with a simple Kafka Consumer & Producer - the
WorkerTaskResult
alter currentTaskRun
in the currentExecution
and changed the status (mostly RUNNING / SUCCEED / FAILED) and also published back toExecution
queue (with Kafka Stream)
As you can see, the Execution
(with TaskRun
list) is the state are current application.
The stream works well when all the message are sequential (no concurrency, I can only have one alter of TaskRun
list at the same time). When the workflow became Parallel (concurrent WorkerTaskResult
can be join), it seems that my Execution state is override and produce a kind of roolback.
Example log output:
...ANSWER
Answered 2020-Apr-22 at 21:54is this pattern (that is not a dag flow as we sink on the same topic) are supported by KafkaStreams ?
In general yes. You just need to make sure that you don't end up with an "infinite loop", i.e., at some point an input record should "terminate" and not produce anything to the output topic any longer. For your case, and Execution
should eventually not create new Tasks
any longer (via the feedback loop).
what is the good way to design this stream to be concurrency safe
It always depends on the concrete application... For your case, if I understand the design of your application correctly, you basically have two input topics (Execution
and WorkerTaskResult
) and two output topics (Execution
and WorkerTask
). When processing the input topics, messages from each input may modify shared state (i.e., a task's state).
Additionally, there is an "outside application" that reads from the WorkerTask
topic and write to the WorkerTaskResult
topic? Hence, there is actually a second loop in you overall data flow? I assume that there are other upstream applications that will actually push new data into the Execution
topic, too?
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