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.
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 using logstash 5.4.2 Persistence Queue. Where I have config file to get input throw JDBC and do some transformations and storing output to mongo db. But when I run this in logstash it inserts only few records let us say 6000 where as the actual output should be 300000 records. And main pipeline gets shutdown. When I see the page file in data folder has more number of records. How to flush the data into output without pipeline before or after shutdown. My logstash persistence queue setting as follows.
pipeline.workers: 2
pipeline.output.workers: 1
pipeline.batch.size: 50
pipeline.batch.delay: 5
pipeline.unsafe_shutdown: false
config.test_and_exit: false
config.reload.automatic: false
queue.type: persisted
queue.page_capacity: 1gb
queue.max_events: 0
queue.max_bytes: 4gb
queue.checkpoint.acks: 1024
queue.checkpoint.writes: 1024
queue.checkpoint.interval: 1000
Is there any way to flush all data from persistence queue to output during main pipeline shutdown or anyway of workaround to handle this issue?
Thanks in Advance!
Your queue.checkpoint.writes is set to 1024, which is a default. You need to set it to 1, to guarantee maximum durability for input events, i.e.
queue.checkpoint.writes: 1
Please remember that this involves heavy disk writing which will impact performance severely.
I am a newbie to Spark Streaming and I have some doubts regarding the same like
Do we need always more than one executor or with one we can do our job
I am pulling data from kafka using createDirectStream which is receiver less method and batch duration is one minute , so is my data is received for one batch and then processed during other batch duration or it is simultaneously processed
If it is processed simultaneously then how is it assured that my processing is finished in the batch duration
How to use the that web UI to monitor and debugging
Do we need always more than one executor or with one we can do our job
It depends :). If you have a very small volume of traffic coming in, it could very well be that one machine code suffice in terms of load. In terms of fault tolerance that might not be a very good idea, since a single executor could crash and make your entire stream fault.
I am pulling data from kafka using createDirectStream which is
receiver less method and batch duration is one minute , so is my data
is received for one batch and then processed during other batch
duration or it is simultaneously processed
Your data is read once per minute, processed, and only upon the completion of the entire job will it continue to the next. As long as your batch processing time is less than one minute, there shouldn't be a problem. If processing takes more than a minute, you will start to accumulate delays.
If it is processed simultaneously then how is it assured that my
processing is finished in the batch duration?
As long as you don't set spark.streaming.concurrentJobs to more than 1, a single streaming graph will be executed, one at a time.
How to use the that web UI to monitor and debugging
This question is generally too broad for SO. I suggest starting with the Streaming tab that gets created once you submit your application, and start diving into each batch details and continuing from there.
To add a bit more on monitoring
How to use the that web UI to monitor and debugging
Monitor your application in the Streaming tab on localhost:4040, the main metrics to look for are Processing Time and Scheduling Delay. Have a look at the offical doc : http://spark.apache.org/docs/latest/streaming-programming-guide.html#monitoring-applications
batch duration is one minute
Your batch duration a bit long, try to adjust it with lower values to improve your latency. 4 seconds can be a good start.
Also it's a good idea to monitor these metrics on Graphite and set alerts. Have a look at this post https://stackoverflow.com/a/29983398/3535853
I have a spark-streaming service, where I am processing and detecting anomalies on the basis of some offline generated model. I feed data into this service from a log file, which is streamed using the following command
tail -f <logfile>| nc -lk 9999
Here the spark streaming service is taking data from port 9999. However, I observe that the last few lines are being dropped, i.e. spark streaming does not receive those log lines or they are not processed.
However, I also observed that if I simply take the logfile as standard input instead of tailing it, no lines are dropped:
nc -q 10 -lk 9999 < logfile
Can anyone explain why this behavior is happening? And what could be a better resolution to the problem of streaming log data to spark streaming instance?
In Spark Streaming, data comes in over the wire, and constitutes a block on every block interval. This block is replicated on other machines (according to your storage level as soon as formed. Once a batch interval elapses, each block formed since the last batch interval tick forms part of a new RDD. It is once you have formed this RDD that you can schedule a job, so the data collected during the batch interval n is then processed during batch interval n+1.
So, the possible culprits for "losing a bit of data towards the end" could be:
you are observing your input file at the same time as you are monitoring the input for Spark. If you consider your monitoring at instant t, a bit after n batch intervals have elapsed, your log file has produced the data for n batches and then some ("a little bit more"). Except, the beginning of the next batch (n+1) is at this stage in the data collection phase, in the form of blocks on your Receiver. No data has been lost, the processing of batch n+1 has simply not started yet.
or your application assumes it's receiving a similar number of elements in each RDD and does not process the potentially (much) smaller last batch's RDD correctly.
or you're stopping your application or data before the last batch interval elapses (you need to wait n+1 batch intervals to see the processing of n batches of data).
or there is something weird occurring with the system clock of your executors. Have you thought of synchronizing them with ntp ?
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