Hello everyone,
Now, this one bugs me for some time and this question got my attention back to this topic.
How can one compare fields over multiple source types without the use of join
, append
or any other subsearch
?
I know, there some use cases where one has to use either of the above commands. But I don't want to and I don't need to, so what can be done?
cheers, MuS
join
or append
are the most intuitive direction to go in, but it's not very efficient and it's pretty cumbersome.
I will show what can be done by using a run everywhere example. This will report on one field user which is available in both sourcetypes and some others group or run_time which is only available in one sourcetype.
The use case here is to show which user, group and run_time we have per date_hour.
Let's start with the obvious one: Have you met chart?
index=_internal earliest=-2h@h latest=-0h@h sourcetype=splunkd OR sourcetype=scheduler | chart values(user) AS user values(group) AS group values(run_time) AS run_time by date_hour
The next obvious will be: Have you met stats?
index=_internal earliest=-2h@h latest=-0h@h sourcetype=splunkd OR sourcetype=scheduler | stats values(user) AS user values(group) AS group values(run_time) AS run_time by date_hour
Now move on for some fancy stats stuff?
index=_internal earliest=-2h@h latest=-0h@h sourcetype=splunkd OR sourcetype=scheduler
| eval hour-{sourcetype}=date_hour
| stats values(hour-*) AS * values(user) AS user values(group) AS group values(run_time) AS run_time
| mvexpand splunkd
| mvexpand scheduler
| where splunkd=scheduler
| rename splunkd AS date_hour
| fields - scheduler
the eval on hour-{sourcetype}=date_hour
will create a new field which looks in this case like hour-splunkd
and hour-scheduler
which will be used later in the where clause....
or another nice run everywhere example:
index=_internal sourcetype="splunkd" OR sourcetype="splunk_web_access" | streamstats count by status, idx, sourcetype | stats values(idx) AS idx, values(status) AS status, values(sourcetype) AS sourcetype | mvexpand status | eval Status = if(match(idx,status), "MATCH", "NO MATCH") | table status, idx, Status
So why all this hustle and not use join instead?
Just for the record, all the above example run on my laptop and Splunk 6 for about 2.5 seconds while the join to get the same result take about 4.5 seconds:
index=_internal sourcetype=splunkd earliest=-2h@h latest=-0h@h | join date_hour [ search index=_internal sourcetype=scheduler earliest=-2h@h latest=-0h@h ] | stats values(date_hour) AS date_hour values(user) AS user values(group) AS group
One more thing: I know that in some use cases one is forced to use join or append, but before that - just give stats a chance .....
cheers, MuS
join
or append
are the most intuitive direction to go in, but it's not very efficient and it's pretty cumbersome.
I will show what can be done by using a run everywhere example. This will report on one field user which is available in both sourcetypes and some others group or run_time which is only available in one sourcetype.
The use case here is to show which user, group and run_time we have per date_hour.
Let's start with the obvious one: Have you met chart?
index=_internal earliest=-2h@h latest=-0h@h sourcetype=splunkd OR sourcetype=scheduler | chart values(user) AS user values(group) AS group values(run_time) AS run_time by date_hour
The next obvious will be: Have you met stats?
index=_internal earliest=-2h@h latest=-0h@h sourcetype=splunkd OR sourcetype=scheduler | stats values(user) AS user values(group) AS group values(run_time) AS run_time by date_hour
Now move on for some fancy stats stuff?
index=_internal earliest=-2h@h latest=-0h@h sourcetype=splunkd OR sourcetype=scheduler
| eval hour-{sourcetype}=date_hour
| stats values(hour-*) AS * values(user) AS user values(group) AS group values(run_time) AS run_time
| mvexpand splunkd
| mvexpand scheduler
| where splunkd=scheduler
| rename splunkd AS date_hour
| fields - scheduler
the eval on hour-{sourcetype}=date_hour
will create a new field which looks in this case like hour-splunkd
and hour-scheduler
which will be used later in the where clause....
or another nice run everywhere example:
index=_internal sourcetype="splunkd" OR sourcetype="splunk_web_access" | streamstats count by status, idx, sourcetype | stats values(idx) AS idx, values(status) AS status, values(sourcetype) AS sourcetype | mvexpand status | eval Status = if(match(idx,status), "MATCH", "NO MATCH") | table status, idx, Status
So why all this hustle and not use join instead?
Just for the record, all the above example run on my laptop and Splunk 6 for about 2.5 seconds while the join to get the same result take about 4.5 seconds:
index=_internal sourcetype=splunkd earliest=-2h@h latest=-0h@h | join date_hour [ search index=_internal sourcetype=scheduler earliest=-2h@h latest=-0h@h ] | stats values(date_hour) AS date_hour values(user) AS user values(group) AS group
One more thing: I know that in some use cases one is forced to use join or append, but before that - just give stats a chance .....
cheers, MuS
@MuS how would this work if one of your dataset is a CSV file, where the CSV file is greater than 50k ? Is it possible to run this sort of query without using |append or join?
You can always use | inputcsv append=t file.csv
in your SPL.
cheers, MuS
@Mus thanks for very useful answer! To avoid messing up this thread I have created new Question which is highly related to this one and is about extending to three searches: https://answers.splunk.com/answers/521078/joining-fields-from-three-different-indexes-and-so.html
I would appreciate your help!
I know I'm late to the party, just wanted to throw in one caution.
It's interesting that streamstats
is actually faster than a join
or append
in this case. I could see that working for a small amount of data, but I suspect that factors like data set size (of both the primary and secondary sources) as well as search mode (single server vs distributed) could have a significant impact on performance.
The overall advice here is great, I just think it's prudent to point out that search commands like transaction
, streamstats
, and eventstats
require that ALL events be streamed to the search head which can have some significant network bandwidth and performance implications. Compare this to stats
where map-reduce allows the data to be precomputed on the indexers. Bottom line, don't forget to look a the "remote search" and the "report search" in the job inspector.
Hi @MuS,
After I POC the 3 of the method, I notice each of these are giving me different numbers of events.
Stats give me 2967 events
Chart give me 3296 events
Join give me 3092 events
Now I am questioning the accuracy of the methodology.
Again provide more Information and details and do it please in your other question.
@MuS -- Where you said, "the eval on hour-{sourcetype}=date_hour will create a new field which looks in this case like hour-splunkd and hour-scheduler which will be used later in the where clause...." I am wondering about that, because in the where clause it says, where splunkd=scheduler so I don't understand that explanation.
Another thing puzzling me is the *values(hour-) AS *** in the stats command. I don't understand at all what that is doing.
In addition try this simple Splunk> Fu search and you will see how stats values(*) AS *
works 🙂
index=_internal earliest=-24h@h latest=-0h@h sourcetype=splunkd OR sourcetype=scheduler
| stats values(sourcet*) AS Splunk>Fu-t* count
Okay, I think I missed something in the first explanation that is now cleared up (maybe!). So the use of brackets {} around an existing field yields the possible values for that field in that search in an eval. So you use those appended to something to create new field names to which you can assign anything I suppose, but date_hour in the example.
So it seems that in the stats command (and perhaps elsewhere) use of a partial field name followed by a * will cause splunk to auto-complete all possible field names with that specified beginning.
But in the rename Splunk>fu-t* it looks like the * autocompletes based on what ending was previously matched, which in this case is ype. Have I got it?
@wrangler2x, both is correct.
@wrangler2x
HeHe, this can be really mind twisting, but it's not - let me explain:
| eval hour-{sourcetype}=date_hour
will create fields that are named hour-splunkd
or hour-scheduler
with the value of the corresponding date_hour
field of splunkd
or scheduler
. Next the stats
will use the hour-*
fields like this:
| stats values(hour-*) AS *
and renames them into the value of star; hour-splunkd
becomes splunkd
and hour-scheduler
becomes scheduler
- Does that make sense?
Can you apply this same method to search across multiple indices instead of multiple sourcetypes?
Yes, any combination of search filters.
Thanks for clearing that up!
Oh, that is twisted sister! Yes, makes perfect sense. I had no idea the star could be used that way. That's pretty wow, and I see how it allows you access to the data for the two sourcetypes.
So, I take it then that when you do where splunkd=scheduler that you are pairing the data_hour for each set of information?
@wrangler2x: correct.
I've occasionally found eventstats
to be particularly helpful as well.
append0 a chance ....
append0?
HeHe that is funny, is the 'raw' text it says just give stats a chance
but in the answer it is append()
?!???
For sure it should also say ...give stats a chance
😉