Splunk Search

How do I find all duplicate events?

Splunk Employee
Splunk Employee

I suspect that I may have duplicate events indexed by Splunk. The cause may be my originating files having dupes OR my Splunk configuration may be indexing some events twice or more times.

To be sure, what search can I run to find all my duplicate events currently within my Splunk index?

1 Solution

Super Champion

I think it's safe to assume that if an event is duplicated (same value for _raw) than the duplicates and the original should have the same timestamp. Therefore, it should be possible to include maxspan=1s, like so:

... | eval dupfield=_raw | transaction dupfield maxspan=1s keepevicted=true

I'm not sure about Gerald's comment about multi-line events, since my de-dedup catching was limited to single line events, but it seems to me that some kind of sed trick could be used, like so:

... | eval dupfield=_raw | rex mode=sed field=dupfield "s/[\r\n]/<EOL>/g" | transaction dupfield maxspan=1s keepevicted=true

BTW, I found the transaction based approach to be much faster than using stats approach suggested in the comments above and much less restrictive. (It seems like stats has a a 10,000 entry limit on the "by" clause)


Also, in my case I was trying to not only get a count of duplicate events but figure out the extra volume (in bytes) that could have been avoided if the data was de-duped externally before being loaded. I used a search like this:

sourcetype=my_source_type | rename _raw as raw | eval raw_bytes=len(raw) | transaction raw maxspan=1s keepevicted=true | search eventcount>1 | eval extra_events=eventcount-1 | eval extra_bytes=extra_events*raw_bytes | timechart span=1d sum(extra) as exta_events, sum(eval(extra_bytes/1024.0/1024.0)) as extra_mb

This shows you the impact in megabytes per day.

View solution in original post

Influencer

I hate to dig up an old thread but this appears ion the most voted list. Is it still valid to say that transaction is more effective than stats in this instance?

For example the following search should return the same results

sourcetype=* |  streamstats count as dupes by _time,_raw 
| search dupes> 1
| stats count as extra_events by _raw,host,source 
| eval raw_bytes=len(_raw) |eval extra_mb=extra_events*raw_bytes/1024 
| stats sum(extra_events) as extra_events, sum(extra_mb) values(source) by source

I ran a 4 hour search over one of our higher volume indexes , and it took about 10 minutes to run over about 30 million events. Using the accepted answer to this question, I cancelled the search when it was 3% complete after 15 minutes.

So is transaction no longer appropriate for finding duplicates? Or is this an edge case for very large indices.

0 Karma

Engager

It would be very nice to have an answer to this question, as I've seen similar numbers. As jplumsdaine22 also points out, if this is a dead thread a pointer to a better dupe search resource would be valuable. Thanks!

0 Karma

Path Finder

Hi folks!

I am working with a problem where one transaction may get logged several times and I would need to find events with identical transactionIDs. What I manage to do is

index=myindex loglines STATUS_CODE=200 
| top TRXID 
| search count > 1

This gives me the transactions that have been multiple times logged, but when I try doing what is suggested earlier I only find identical log lines.

index=myindex loglines STATUS_CODE=200
| eval dupfield = _raw 
| transaction dupfield maxspan=1m keepevicted=true  
| search eventcount > 1
| eval extra_events=eventcount-1  
| stats sum(extra_events) as extra_events by CLIENTID

What I am trying to find out, how to tell transaction to treat two lines to be associated to one single transaction. The TRXID and CLIENTID mentioned in the examples are present on all the lines matched with the keyword loglines.

0 Karma

Path Finder

I know it's an old topic but I'll chip in as well here. In addition to dmaislin_splunk's suggestions I've added source values to show the two or more sources where the duplicates are. This gives me better depth of understanding as to why items are duplicated (e.g. are they in a cluster?)

sourcetype=* 
| rename _raw as raw 
| eval raw_bytes=len(raw) 
| transaction raw maxspan=1s keepevicted=true 
| search eventcount>1 
| eval extra_events=eventcount-1 
| eval extra_bytes=extra_events*raw_bytes 
| stats sum(extra_events) as extra_events, sum(eval(extra_bytes/1024.0/1024.0)) as extra_mb values(source) by source 
| rename "values(source)" as "Duplicated in" 

Regards,

Ken

Splunk Employee
Splunk Employee

Original fixed due to some typos:

sourcetype=* | rename raw as raw | eval rawbytes=len(raw) | transaction raw maxspan=1s keepevicted=true | search eventcount>1 | eval extraevents=eventcount-1 | eval extrabytes=extraevents*rawbytes | timechart span=1s sum(extraevents) as extraevents, sum(eval(extrabytes/1024.0/1024.0)) as extramb

To show number of events and size by sourcetype:

sourcetype=* | rename raw as raw | eval rawbytes=len(raw) | transaction raw maxspan=1s keepevicted=true | search eventcount>1 | eval extraevents=eventcount-1 | eval extrabytes=extraevents*rawbytes |stats sum(extraevents) as extraevents, sum(eval(extrabytes/1024.0/1024.0)) as extramb by host,sourcetype

Super Champion

I think it's safe to assume that if an event is duplicated (same value for _raw) than the duplicates and the original should have the same timestamp. Therefore, it should be possible to include maxspan=1s, like so:

... | eval dupfield=_raw | transaction dupfield maxspan=1s keepevicted=true

I'm not sure about Gerald's comment about multi-line events, since my de-dedup catching was limited to single line events, but it seems to me that some kind of sed trick could be used, like so:

... | eval dupfield=_raw | rex mode=sed field=dupfield "s/[\r\n]/<EOL>/g" | transaction dupfield maxspan=1s keepevicted=true

BTW, I found the transaction based approach to be much faster than using stats approach suggested in the comments above and much less restrictive. (It seems like stats has a a 10,000 entry limit on the "by" clause)


Also, in my case I was trying to not only get a count of duplicate events but figure out the extra volume (in bytes) that could have been avoided if the data was de-duped externally before being loaded. I used a search like this:

sourcetype=my_source_type | rename _raw as raw | eval raw_bytes=len(raw) | transaction raw maxspan=1s keepevicted=true | search eventcount>1 | eval extra_events=eventcount-1 | eval extra_bytes=extra_events*raw_bytes | timechart span=1d sum(extra) as exta_events, sum(eval(extra_bytes/1024.0/1024.0)) as extra_mb

This shows you the impact in megabytes per day.

View solution in original post

Communicator

Stephen it would be nice if there was a search command that could remove duplicates -1, I'm not what the impact would be.
* | tag_dupes | delete

Splunk Employee
Splunk Employee

Lowell is absolutely right that this transaction will be MUCH, MUCH faster than anything involving stats because of its favorable eviction policy. Transaction, especially with maxspan set, will only keep data for the current second in memory, as search scans backwards through time.

Splunk Employee
Splunk Employee

Try appending this search string to your current search to find duplicates:

| transaction fields="_time,_raw" connected=f keepevicted=t | search linecount > 1

Splunk Employee
Splunk Employee

+1, needed. Has it been filed?
(Don't forget to accept your current answer, unless it doesn't satisfy.)

0 Karma

Splunk Employee
Splunk Employee

Agreed. showdupes filter=all|latest would be very beneficial, especially when debugging input configs.

Splunk Employee
Splunk Employee

Actually now that I think about it: | stats count by _time,_raw | rename _raw as raw | where count > 1 might be better. But an ER for search command to showdupes might be best.

Splunk Employee
Splunk Employee

This won't work if the original data is multiline. But you could fix that with | rename duration as original_duration | transaction _time,_raw | search duration=* The transaction will also be rather more efficient if you set maxspan=0 and maxopentxn=1 if your duplicates will be consecutive.