Based on the Flink Scala quickstart, I've created a sample job that uses org.apache.flink.streaming.connectors.twitter.TwitterSource.
I'm using a local streaming job manager /start-local-streaming.sh, and start the job with ~/flink-0.10.0/bin/flink run target/quickstart-0.1.jar
In the TwitterSource sourcecode, I notice that there are logging statements of the INFO level. How can I get those logged to console?
The Flink daemons are started in the background, that's why Flink is logging to log files in the log/ directory.
To monitor these files, use tail -f log/*. This will print the log entries on the console.
Another approach would be to change the start scripts of Flink to start it in the foreground + change the conf/log4j.properties file to use the ConsoleAppender.
Related
I see many docs and posts about how to send logs to Stackdriver but almost no information about how to do the opposite - send logs from the Stackdriver to Kafka.
In my case, our Ops want to collect the logs from our web servers using Google's stackdriver agents and pushing them to stackdriver ... However, for my stream processing needs I want to get the logs into Kafka to use it's unparalleled abilities to retain and reprocess data by any number of consumers, something that I cannot do with PubSub.
So, what are the options for doing this? I only saw a couple of possible avenues - neither sounds too good:
based on this post: (https://powerspace.tech/how-to-stream-data-from-google-pubsub-to-kafka-with-kafka-connect-dbef1c340a76) push data into PubSub first, and then read from it using either Kafka connector or write my own Kafka consumer. I hate the thought of adding yet another hop (serialize/deserialize/ack/etc.) between the source of data and Kafka ....
I noticed a brief mentioning in passing on adding a plugin to Google's version of Fluentd (which is what stackdriver log collection agent is based on) here: https://powerspace.tech/how-to-stream-data-from-google-pubsub-to-kafka-with-kafka-connect-dbef1c340a76 . Not many details - so hard to tell how involved this approach is ...
Any other options?
Thank you!
Enter in to the Kafka console and add certain elements in the console. Once you have added the elements in the Kafka console you need to check if these elements are reflected successfully in the cloud shell. For this you will run the command > $ gcloud pubsub subscriptions pull from-kafka — auto-ack — limit=10 < . Once you run this command it will take some time to sync with the Kafka console. You will get the results after running this command a couple of times.
You will run the commands in the Cloud Shell and see the output in the Kafka VM SSH.
***Image1
Now you will be verifying the exact opposite procedure where in you will be running the command in the Kafka VM and seeing the output in the Cloud Shell. It will take some time for the output to be reflected and you may have to run the command > $ gcloud pubsub subscriptions pull from-kafka — auto-ack — limit=10 < a couple of times to see the output. Your output will look like this
*** image2
The Kafka plugin is deprecated. For more information, refer to https://cloud.google.com/stackdriver/docs/deprecations
Note: This functionality is only available for agents running on Linux. It is not available on Windows.
Kafka is monitored via JMX. Monitoring supports monitoring Kafka version 0.8.2 and higher.
On your VM instance, download kafka-082.conf from the GitHub configuration repository and place it in the directory /etc/stackdriver/collectd.d/:
(cd /etc/stackdriver/collectd.d/ && sudo curl -O https://raw.githubusercontent.com/Stackdriver/stackdriver-agent-service-configs/master/etc/collectd.d/kafka-082.conf)
The downloaded plugin configuration file assumes that your Kafka server is configured to accept JMX connections on port 9999. If you have configured Kafka with a different JMX port, as root, edit the file and follow the instructions to change the JMX port settings.
After adding the configuration file, restart the Monitoring agent by running the following command:
sudo service stackdriver-agent restart
What is monitored:
https://cloud.google.com/monitoring/api/metrics_agent#agent-kafka
I'm trying to verify that shutdown is completing cleanly on Kubernetes, with a .NET Core 2.0 app.
I have an app which can run in two "modes" - one using ASP.NET Core and one as a kind of worker process. Both use Console and JSON-which-ends-up-in-Elasticsearch-via-Filebeat-sidecar-container logger output which indicate startup and shutdown progress.
Additionally, I have console output which writes directly to stdout when a SIGTERM or Ctrl-C is received and shutdown begins.
Locally, the app works flawlessly - I get the direct console output, then the logger output flowing to stdout on Ctrl+C (on Windows).
My experiment scenario:
App deployed to GCS k8s cluster (using helm, though I imagine that doesn't make a difference)
Using kubectl logs -f to stream logs from the specific container
Killing the pod from GCS cloud console site, or deleting the resources via helm delete
Dockerfile is FROM microsoft/dotnet:2.1-aspnetcore-runtime and has ENTRYPOINT ["dotnet", "MyAppHere.dll"], so not wrapped in a bash process or anything
Not specifying a terminationGracePeriodSeconds so guess it defaults to 30 sec
Observing output returned
Results:
The API pod log streaming showed just the immediate console output, "[SIGTERM] Stop signal received", not the other Console logger output about shutdown process
The worker pod log streaming showed a little more - the same console output and some Console logger output about shutdown process
The JSON logs didn't seem to pick any of the shutdown log output
My conclusions:
I don't know if Kubernetes is allowing the process to complete before terminating it, or just issuing SIGTERM then killing things very quick. I think it should be waiting, but then, why no complete console logger output?
I don't know if console output is cut off when stdout log streaming at some point before processes finally terminates?
I would guess that the JSON stuff doesn't come through to ES because filebeat running in the sidecar terminates even if there's outstanding stuff in files to send
I would like to know:
Can anyone advise on points 1,2 above?
Any ideas for a way to allow a little extra time or leeway for the sidecar to send stuff up, like a pod container termination order, delay on shutdown for that container, etc?
SIGTERM does indeed signal termination. The less obvious part is that when the SIGTERM handler returns, everything is considered finished.
The fix is to not return from the SIGTERM handler until the app has finished shutting down. For example, using a ManualResetEvent and Wait()ing it in the handler.
I've started to look into this for my own purposes and have come across your question over a year after it was posted... This is a bit late, but have you tried GraceTerm?
There is an associated NuGET package for this.
From the description...
Graceterm middleware provides implementation to ensure graceful shutdown of AspNet Core applications. The basic concept is: After application received a SIGTERM (a signal asking it to terminate), Graceterm will hold it alive till all pending requests are completed or a timeout occur.
I haven't personally tried this yet, but it does look promising.
Try add STOPSIGNAL SIGINT to your Dockerfile
I have an Apache Spark service instance on IBM cloud(light plan). After I submit a Spark job I want to see its progress, it would be perfect to see it the Spark way - get the Spark progress UI with number of partitions and everything. I would also like to get a connection to the history server.
I saw that I can run ./spark-submit.sh ... --status <app id> but I would like to get something more informative.
I saw the comment
You can track the current execution of your running application and see the details of previously run jobs on the Spark job history UI by clicking Job History on the Analytics for Apache Spark service console.
here, but fail to understand where exactly I get this console/history thing.
As a side note, is there any detailed technical documentation of this service, e.g. number of concurrent jobs which can run, technology stack etc..?
As per spark Documentation:
Every SparkContext launches a web UI, by default on port 4040, that displays useful information about the application. This includes:
A list of scheduler stages and tasks
A summary of RDD sizes and memory usage
Environmental information.
Information about the running executors
You can access this interface by simply opening http://{driver-node}:4040 in a web browser. If multiple SparkContexts are running on the same host, they will bind to successive ports beginning with 4040 (4041, 4042, etc).
Bottom Line : http://{driver-node}:4040 (replace driver-node with the node where spark job invoked) and you should be good to go
I'm trying to build an Apache Spark application that normalizes csv files from HDFS (changes delimiter, fix broken lines). I use log4j for logging but all the logs just print in the executors so the only way i can check them is using yarn logs -applicationId command. Is there any way i can redirect all logs( from driver and from executors) to my gateway node(the one which launchs the spark job) so i can check them during execution?
You should have the executors log4j props configured to write files local to themselves. Streaming back to the driver will cause unnecessary latency in processing.
If you plan on being able to 'tail" the logs in near real-time, you would need to instrument a solution like Splunk or Elasticsearch, and use tools like Splunk Forwarders, Fluentd, or Filebeat that are agents on each box that specifically watch for all configured log paths, and push that data to a destination indexer, that'll parse and extract log field data.
Now, there are other alternatives like Streamsets or Nifi or Knime (all open source), which offer more instrumentation for collecting event processing failures, and effectively allow for "dead letter queues" to handle errors in a specific way. The part I like about those tools - no programming required.
i think it is not possible. When you execute spark in local mode you can able to see it in console. Otherwise you have to alter log4j properties for the log file path.
As per https://spark.apache.org/docs/preview/running-on-yarn.html#configuration,
YARN has two modes for handling container logs after an application has completed. If log aggregation is turned on (with the yarn.log-aggregation-enable config in yarn-site.xml file), container logs are copied to HDFS and deleted on the local machine.
You can also view the container log files directly in HDFS using the HDFS shell or API. The directory where they are located can be found by looking at your YARN configs (yarn.nodemanager.remote-app-log-dir and yarn.nodemanager.remote-app-log-dir-suffix in yarn-site.xml).
I am not sure whether the log aggregation from worker nodes happen in real time !!
There is an indirect way to achieve. Enable the following property in yarn-site.xml.
<property>
<name>yarn.log-aggregation-enable</name>
<value>true</value>
</property>
This will store all your logs of the submitted applications in hdfs location. Then using the following command you can download the logs into a single aggregated file.
yarn logs -applicationId application_id_example > app_logs.txt
I came across this github repo which downloads the driver and container logs separately. Clone this repository : https://github.com/hammerlab/yarn-logs-helpers
git clone --recursive https://github.com/hammerlab/yarn-logs-helpers.git
In your .bashrc (or equivalent), source .yarn-logs-helpers.sourceme:
$ source /path/to/repo/.yarn-logs-helpers.sourceme
Then download the aggregated logs into nicely segregated driver and container logs by this command.
yarn-container-logs application_example_id
I have pseudo distributed hadoop 2.2.0 Environment setup in my laptop.I can run mapreduce applications(including Pig and Hive jobs) and the status of the applications can be seen from the Web UI http://localhost:8088
I have downloaded the Spark library and just used the file system-HDFS for the spark applications.when I launch a spark application,it is getting launched and the execution also gets completed successfully as expected.
But the Web UI http://localhost:8088 is not listing the Spark application completed/launched.
Please suggest if there is any other additional configuration is required for seeing Spark applications in the Web UI.
(Note: http://localhost:50070this Web UI shows the files correctly,when tried writing files to HDFS via Spark applications)
You might have figured it out but for others who are starting with Spark.You can see all the spark jobs on
http://localhost:4040
after your spark context is initiated(port can be different eg 4041). Based on Standalone installation you can see the master and slave status on
http://localhost:8080
(for slave port is usually 8081 onward). you need to Spark-submit jobs to yarn-cluster or client to see the same on hadoop webservices.