To be Precise, I am handling a log file which has almost millions of records. Since it is a Billing Summary log, Customer Information will be recorded in no particular order.
I am Using customized GROK Patterns and logstash XML filter plugin to extract the data which would be sufficient to track. To track the The Individual Customer Activities, I am using "Customer_ID" as a unique key. So Even though I am using Multiple Logstash Files, and Multiple GROK Patterns, All his Information could be bounded/Aggregated using his "Customer_ID" (Unique Key)
here is my sample of log file,
7-04-2017 08:49:41 INFO abcinfo (ABC_RemoteONUS_Processor.java52) - Customer_Entry :::<?xml version="1.0" encoding="UTF-8"?><ns2:ReqListAccount xmlns:ns2="http://vcb.org/abc/schema/"/"><Head msgId="1ABCDEFegAQtQOSuJTEs3u" orgId="ABC" ts="2017-04-27T08:49:51+05:30" ver="1.0"/><Cust id="ABCDVFR233cd662a74a229002159220ce762c" note="Account CUST Listing" refId="DCVD849512576821682" refUrl="http://www.ABC.org.in/" ts="2017-04-27T08:49:51+05:30"
My Grok Pattern,
grok {
patterns_dir => "D:\elk\logstash-5.2.1\vendor\bundle\jruby\1.9\gems\logstash-patterns-core-4.0.2\patterns"
match => [ "message" , "%{DATESTAMP:datestamp} %{LOGLEVEL:Logseverity}\s+%{WORD:ModuleInfo} \(%{NOTSPACE:JavaClass}\)%{ABC:Customer_Init}%{GREEDYDATA:Cust}"]add_field => { "Details" => "Request" }remove_tag => ["_grokparsefailure"]}
My Customized pattern which is stored inside Pattern_dir,
ABC ( - Customer_Entry :::)
My XML Filter plugin,
xml {
source => "Cust"
store_xml =>false
xpath => [
"//Head/#ts", "Cust_Req_time",
"//Cust/#id", "Customer_ID",
"//Cust/#note", "Cust_note", ]
}
So whatever the details comes behind ** - Customer_Entry :::**, I will be able to extract it using XML Plugin Filter (will be stored similar to multi-line codec). I have written 5 different Logstash files to extract different Activities of Customer with 5 different Grok Patterns. Which will tell,
1.Customer_Entry
2.Customer_Purchase
3.Customer_Last_Purchase
4.Customer_Transaction
5.Customer_Authorization
All the above Grok patterns has different set of Information, which will be grouped by Customer_ID as I said earlier.
I can able to Extract the Information and Visualize It clearly in Kibana without any flaw by using my Customized pattern with different log files.
Since I have 100's of Log files each and everyday to put into logstash, I opted for Filebeats, but Filebeats run with only one port "5044". I tried to run with 5 different ports for 5 different logstash files but that was not working, Only one logstash file of 5 was getting loaded rest of the config files were being Idle.
here is my sample filebeat output.prospector,
output.logstash:
hosts: ["localhost:5044"]
output.logstash:
hosts: ["localhost:5045"]
output.logstash:
hosts: ["localhost:5046"]
I couldn't add all the grok patterns in one logstash config file, because XML Filter plugin takes the source "GREEDYDATA". in such case I will be having 5 different Source=> for 5 different Grok pattern.
I even tried that too but that was not working.
Looking for better approach.
Sounds like you're looking for scale, with parallel ingestion. As it happens, File beats supports something called load-balancing which sounds like what you're looking for.
output.logstash:
hosts: [ "localhost:5044", "localhost:5045", "localhost:5046" ]
loadbalance: true
That's for the outputs. Though, I believe you wanted multithreading on the input. FileBeats s supposed to track all files specified in the prospector config, but you've found limits. Globbing or specifying a directory will single-thread the files in that glob/directory. If your file-names support it, creative-globbing may get you better parallelism by defining multiple globs in the same directory.
Assuming your logs are coming in by type:
- input_type: log
paths:
- /mnt/billing/*entry.log
- /mnt/billing/*purchase.log
- /mnt/billing/*transaction.log
Would enable prospectors on multiple threads reading in parallel files here.
If your logs were coming in with random names, you could use a similar setup
- input_type: log
paths:
- /mnt/billing/a*
- /mnt/billing/b*
- /mnt/billing/c*
[...]
- /mnt/billing/z*
If you are processing lots of files with unique names that never repeat, adding the clean_inactive config config-option to your prospectors will keep your FileBeat running fast.
- input_type: log
ignore_older: 18h
clean_inactive: 24h
paths:
- /mnt/billing/a*
- /mnt/billing/b*
- /mnt/billing/c*
[...]
- /mnt/billing/z*
Which will remove all state for files older than 24 hours old, and won't bother processing any file more than 18 hours old.
Related
Currently, I have for example the following logs messages based on different names/id (something like that, it is generated based on the specific pattern that is set in the code)
example:
log from one source named "container-ABC":
"This is the example log, id: 123abc, name:container-ABC"
"This is the example log, id: 456def, name:container-ABC"
So in rsyslog, how can I split the source into multiple log files? But note that it is generated based on specific a preset pattern regex and does not have a fixed id.
message has "id: 123abc" -> into 123abc.log
message has "id: 456def" -> into 456def.log
Thank you.
I recently started working with Packetbeat.
For my use-case, I only need some specific fields (to the point where if I could I would completely rewrite the mapping, but am leaving that as a last resort).
I tried removing some of the fields from the "dns.answers" array of objects, but what I did doesn't seem to have any effect:
- include_fields:
fields:
- dns.question.name
- dns.question.type
- dns.answers
- dns.answers_count
- dns.resolved_ip
- drop_fields:
fields:
- dns.answers.name
In addition, I also tried including only the fields I want but that didn't seem to work either, e.g:
- include_fields:
fields:
- dns.question.name
- dns.question.type
- dns.answers.data
- dns.answers_count
- dns.resolved_ip
Any ideas? If rewriting the template/mapping of the index is the best choice, or perhaps using the Ingest Node Pipelines is a better approach, I'd love to hear it.
Thanks
This topic is related but skips the part interesting for me.
I'm using filebeat to read CA Service Desk Manager logs written in a custom format which I cannot change. A single log line looks something like this:
11/07 13:05:26.65 <hostname> dbmonitor_nxd 9192 SIGNIFICANT bpobject.c 2587 Stats: imp(0) lcl(0) rmt(1,1) rmtref(0,0) dbchg(0)
As you can see the date in the beginning has no year information.
I then use logstash to parse the date from the log line. I've got the extra pattern TIMESTAMP defined like this:
TIMESTAMP %{MONTHNUM}[/]%{MONTHDAY}%{SPACE}%{TIME}
And then in logstash.conf I have the following filter:
grok {
patterns_dir => ["./patterns"]
match => { "message" => [
"%{TIMESTAMP:time_stamp}%{SPACE}%{WORD:server_name}%{SPACE}%{DAEMONNAME:object_name}%{SPACE}%{INT:object_id:int}%{SPACE}%{WORD:event_type}%{SPACE}%{USERNAME:object_file}%{SPACE}%{INT:object_line_number:int}%{SPACE}%{GREEDYDATA:log_message}"
]
}
}
date{
match => ["time_stamp", "MM/d HH:mm:ss.SS", "MM/dd HH:mm:ss.SS", "ISO8601"]
}
Currently I have to rely on the automatic timestamp as the time_stamp is indexed as text and I've been fine so far, but occasionally the time the log line was written on the server is not the same as the time it was pushed into ES. Around new year I fear I will run into trouble with this and how the year is deducted from the current time. My questions are:
Is it possible to parse a date field from the data given?
Is there a way to write some advanced logic for the date conversion?
As we're not past the new year yet, is there a way to manually check/ensure the automatic conversion is done correctly?
My processing has a "condense" step before needing further processing:
Source: Raw event/analytics logs of various users.
Transform: Insert each row into a hash according to UserID.
Destination / Output: An in-memory hash like:
{
"user1" => [event, event,...],
"user2" => [event, event,...]
}
Now, I've got no need to store these user groups anywhere, I'd just like to carry on processing them. Is there a common pattern with Kiba for using an intermediate destination? E.g.
# First pass
source EventSource # 10,000 rows of single events
transform {|row| insert_into_user_hash(row)}
#users = Hash.new
destination UserDestination, users: #users
# Second pass
source UserSource, users: #users # 100 rows of grouped events, created in the previous step
transform {|row| analyse_user(row)}
I'm digging around the code and it appears that all transforms in a file are applied to the source, so I was wondering how other people have approached this, if at all. I could save to an intermediate store and run another ETL script, but was hoping for a cleaner way - we're planning lots of these "condense" steps.
To directly answer your question: you cannot define 2 pipelines inside the same Kiba file. You can have multiple sources or destinations, but the rows will all go through each transform, and through each destination too.
That said you have quite a few options before resorting to splitting into 2 pipelines, depending on your specific use case.
I'm going to email you to ask a few more detailed questions in private, in order to properly reply here later.
I have a set of log file which each file log is for specific machine.
What i am trying to achieve is to use the multiline{} filter to join the multi-line messages in each of the file because i would like to have a single #timestamp for each file.
example data in the log file
title
description
test1
test pass
test end
filter {
multiline {
pattern => "from_start_line to end of line"
what => "previous"
negate => true
}
}
I just want to make all the data in the log file as single event without using the pattern.
pretty much like telling logstash to make a multi-line event until EOF.
You can't do like that. Because logstash will always keep monitoring the file. Therefore the EOF is meaningless for it.
The other way you can do is add some pattern to the end of the logs. For example, add log_end to the end of each log output.
title
description
test1
test pass
test end-log_end
Then you can use this pattern to multiline all the logs.
multiline {
pattern => "log_end$"
negate => true
what => "next"
}
Hope this can help you.