I have a log file with 200 MB. I feed the log file into logstash, and it is taking few hours to get the job done.
I am wondering if there's a way to speed things up? Perhaps running it in parallel mode?
You can take a look at here about how to speed up.
The default number of filter workers is 1, but you can increase this number with the '-w' flag on the agent.
For example, If your grok pattern is complex, you can use multiple filter worker(thread) to do the filter task and speed up logstash parsing the logs.
Start with 10 workers like this:
`bin/logstash -f test.conf -w 10`
Will output
Settings: User set filter workers: 10, Default filter workers: 1
Logstash startup completed
Related
I try to feed a csv data file to logstash using filebeat. Unfortunately the messages are out of order. Is there any way to correct this?
Could this caused by TCP or any pipeline? Logstash started logstash.javapipeline / pipeline_id=>"main", "pipeline.workers"=>8
I tried:
filebeat - output to console - pass
filebeat - output to logstash (localhost) - logstash w/o filter; output to stdout - fail (wrong order of messages)
Per default order is not guaranteed in Logstash as the events in the batch can be reordered in the filter processing and some events can be processed faster than others.
If you need to order your events you will have to change the number of pipeline.workers to 1, which means that only 1 CPU will be used to process your messages.
Also, set pipeline.ordered to auto in logstash.yml.
Setting pipeline.workers to 1 will make logstash process the events in the orders they are received, but since it will use only 1 CPU, it can impact the performance if you have a high rate of events per second.
This is the part of the documentation about ordering events.
I am running slurm job arrays with --array, and I would like to run about 2000 tasks/array items. However this is beyond the cluster's job submission limit of ~500 at a time.
Are there any tips/best practices for splitting this up? I'd like to submit it all at once and still be able to pass the array id arguments 1-2000 to my programs if possible. I think something like waiting to submit pieces of the array might be helpful but I'm not sure how to do this at the moment.
If the limit is on the size of an array:
You will have to split the array into several job arrays. The --array parameter accepts values of the form <START>-<END> so you can submit four jobs:
sbatch --array=1-500 ...
sbatch --array=501-1000 ...
sbatch --array=1001-1500 ...
sbatch --array=1501-200 ...
This way you will bypass the 500-limit and still keep the SLURM_ARRAY_TASK_ID ranging from 1 to 2000.
To ease things a bit, you can write this all in one line like this:
paste -d- <(seq 1 500 2000) <(seq 500 500 2000) | xargs -I {} sbatch --array={} ...
If the limit is on the number of submitted jobs:
Then one option is to have the last job of the array submit the following chunk.
#!/bin/bash
#SBATCH ...
...
...
if [[ $((SLURM_ARRAY_TASK_ID % 500)) == 0 ]] ; then
sbatch --array=$((SLURM_ARRAY_TASK_ID+1))-$((SLURM_ARRAY_TASK_ID+500)) $0
fi
Note that ideally, the last running job of the array should submit the job, and it may or may not be the one with the highest TASK ID, but this has worked for all practical purposes in many situations.
Another options is to setup a cron job to monitor the queue and submit each chunk when possible, or to use a workflow manager that will that for you.
you can run a script to submit your jobs and try to make the program sleep a few seconds after every 500 submissions. see https://www.osc.edu/resources/getting_started/howto/howto_submit_multiple_jobs_using_parameters
I have set of an array job as follows:
sbatch --array=1:100%5 ...
which will limit the number of simultaneously running tasks to 5. The job is now running, and I would like to change this number to 10 (i.e. I wish I'd run sbatch --array=1:100%10 ...).
The documentation on array jobs mentions that you can use scontrol to change options after the job has started. Unfortunately, it's not clear what this option's variable name is, and I don't think it is listed in the documentation of the sbatch command here.
Any pointers well received.
You can change the array throttling limit with the following command:
scontrol update ArrayTaskThrottle=<count> JobId=<jobID>
I was looking at the logstash pipeline.workers option which states that
-w, --pipeline.workers COUNT
Sets the number of pipeline workers to run. This option sets the number of workers that will, in parallel, execute the filter and output stages of the pipeline. If you find that events are backing up, or that the CPU is not saturated, consider increasing this number to better utilize machine processing power. The default is the number of the host’s CPU cores.
I was wondering if logstash input stage also uses all the cores of my machine:
input {
kafka {
bootstrap_servers=>"kfk1:9092,kfk2:9092"
topics => ["mytopic"]
group_id => "mygroup"
key_deserializer_class => "org.apache.kafka.common.serialization.ByteArrayDeserializer"
value_deserializer_class => "org.apache.kafka.common.serialization.ByteArrayDeserializer"
codec => avro {
schema_uri => "/apps/schema/rocana3.schema"
}
}
}
Does this input > kafka > codec > avro also utilizes all the cores of my machine or this a single threaded stage?
Logstash input pipelining has a few quirks in it. It can be multithreaded, but it takes some configuration. There are two ways to do it:
The input plugin has a workers parameter, not many do.
Each input {} block will run on its own thread.
So, if you're running the file {} input plugin, which lacks a worker config option, each file you define will be serviced by one and only one thread.
Codecs run in the context of the plugin that calls them, which typically are single-threaded per invocation.
Where most Logstash deployments I've run into end up using many cores is in the filter {} stage of the pipeline, not the input. That is why Logstash provides a way to configure the number of pipeline workers. For an input, or set of inputs, pulling thousands of events a second, you can load up a box pretty far solely on the filter {} to output {} pipeline.
Logstash is running.
How long takes it from adding a single line to a log file until Logstash recognize the new line and start to transform and output it.
With a simple BASH script I measure from 99 msec up to 800 msec including a transformation. It's clear that the latency depends on the Logstash transformation, HD, OS and the CPU. But how recognize Logstash the file change? Is there an internal timer? Pulls logstash from file?
Logstash's file input polls the files being watched at the interval set in the stat_interval parameter, which currently (Logstash 1.5) defaults to 1, i.e. every second.
In other words, assuming that
Logstash isn't behind on the reading any of the log files monitored by a particular file input and
the Logstash process isn't CPU-starved (it usually runs at priority 19 so heavy CPU usage by other processes could cause scheduling delays),
new events will on average get picked up within 500 ms and in the worst case within 1000 ms.