This is not a question about how to capture logging on celery workers. Is there any way to capture celery logging on a Producer. What I want is to capture every log that get generated by celery on the Producer when I call task.delay(...) or task.apply_async(...).
EDIT:
I don't want to capture worker logs on producer. I want to capture everything that happen in celery from the time of my call to apply_async until the task is sent to the broker.
No, there is no way to capture worker logs on the producer. All you get is the exception, if thrown. Any logging is happening on the worker side, so you have to examine logs of that particular worker, or if you use some centralised log system then you have to look for logs from that worker...
Update: seems like you want to capture eventual logging from Celery on the producer (client) side. As far as I know Celery and the underlying transport handling library (Kombu) do not log anything. I could be wrong of course, but I can't remember seeing any logging there and I have read Celery (Kombu not that much to be fair) code many times...
A possible solution is to make Celery workers send logs to some centralised system that your Celery client can access...
I am going through kafka connect, and i am trying to get the concepts.
Let us say I have kafka cluster (nodes k1, k2 and k3) setup and it is running, now i want to run kafka connect workers in different nodes say c1 and c2 in distributed mode.
Few questions.
1) To run or launch kafka connect in distributed mode I need to use command ../bin/connect-distributed.sh, which is available in kakfa cluster nodes, so I need to launch kafka connect from any one of the kafka cluster nodes? or any node from where I launch kafka connect needs to have kafka binaries so that i will be able to use ../bin/connect-distributed.sh
2) I need to copy the my connector plugins to any kafka cluster node( or to all cluster nodes?) from where I do the step 1?
3) how does kafka copies these connector plugins to worker node before starting jvm process on the worker node? because the plugin is the one which has my task code and it needs to be copied to worker in order to start the process in worker.
4) Do i need to install anything in connect cluster nodes c1 and c2, like need to install java or any kafka connect related?
5) In some places it says use confluent platform but i would like to start it with apache kafka connect alone first.
can some one please through some light or even pointer to some resources would also help.
Thank you.
1) In order to have a highly available kafka-connect service you need to run at least two instances of connect-distributed.sh on two distinct machines that have the same group.id. You can find more details regarding the configuration of each worker here. For improved performance, Connect should be ran independently of the broker and Zookeeper machines.
2) Yes, you need to place all your connectors under plugin.path (normally under /usr/share/java/) on every machine that you are planning to run kafka-connect.
3) kafka-connect will load the connectors on startup. You don't need to handle this. Note that if your kafka-connect instance is running and a new connector is added, you need to restart the service.
4) You need to have Java installed on all your machines. For Confluent Platform particularly:
Java 1.7 and 1.8 are supported in this version of Confluent Platform
(Java 1.9 is currently not supported). You should run with the
Garbage-First (G1) garbage collector. For more information, see the
Supported Versions and Interoperability.
5) It depends. Confluent was founded by the original creators of Apache Kafka and it comes as a more complete distribution adding schema management, connectors and clients. It also comes with KSQL which is quite useful if you need to act on certain events. Confluent simply adds on top of the Apache Kafka distribution, it's not a modified version.
Answer given by Giorgos is correct. I ran few connectors and now I understand it better.
I am just trying to put it differently.
In Kafka connect there are two things involved one is Worker and second is connector.Below is on details about running distributed Kafka connect.
Kafka connect Worker is a Java process on which the connector/connect task will run. So first thing is we need to launch worker, to run/launch a worker we need java installed on that machine then we need Kafka connect related sh/bat files to launch worker and kafka libs which will be used by kafka connect worker, for this we will just simply copy/install Kafka in the worker machine, also we need to copy all the connector and connect-task related jars/dependencies in "plugin.path" as defined in the below worker properties file, now worker machine is ready, to start worker we need to invoke ./bin/connect-distributed.sh ./config/connect-distributed.properties, here connect-distributed.properties will have configuration for worker. The same thing has to be repeated in each machine where we need to run Kafka connect.
Now the worker java process is running in all machines, the woker config will have group.id property, the workers which have this same property value will be forming a group/cluster of workers.
Each worker process will expose rest endpoint (default http://localhost:8083/connectors), to launch/start a connector on the running workers, we need do http-post a connector config json, based on the given config the worker will start the connector and the number of tasks in the above group/cluster workers.
Example: Connect post,
curl -X POST -H "Content-Type: application/json" --data '{"name": "local-file-sink", "config": {"connector.class":"FileStreamSinkConnector", "tasks.max":"3", "file":"test.sink.txt", "topics":"connect-test" }}' http://localhost:8083/connectors
I'm working on a cluster that uses SGE to manage jobs across the worker nodes. Is there a way to use the SGE queue as the broker in a way that will cooperate with other people submitting jobs through non-celery means. I currently use python-gridmap to submit python jobs to the SGE queue but I'd like to use the feature-set from Celery.
Would I need to make a new Broker, or Consumer, both?
I have the main producer of tasks in a webserver. I do not want the webserver to consume any tasks, so it should only send the tasks to the broker which get consumed by other nodes.
Right now I route tasks using the -Q option in the nodes by specifying the particular queues for each node. Is there a way to specify 0 queues for a worker?
Any help appreciated, thanks!
You do not need to use a worker to push tasks to the broker - you can do that from a regular python process.
I am using celery 3.0.13 and using mongodb as the broker.
I am trying to purge the tasks in a custom queue. I stopped all the workers and I tried to purge waiting tasks in the custom queue using the "celery purge" command but the command reports that no tasks were purged. I rechecked that the tasks are still in the queue even after running the commands (using flower).
Am I missing anything?
Thanks.
It may be related to this issue:
https://github.com/celery/celery/issues/1047