I have 3 grok patterns that I want to test on a single log file. How do I make sure that all my log lines are being matched?
I already tried https://grokconstructor.appspot.com/do/match, which seems to only allow me to try one pattern at a time.
If there is some kind of setting that shows me percentage of matches in logstash that would be great.
or if someone know how I can use the grokcontructor website with multiple patterns against one log file.
I hope those three patterns can't be tested on a single log file. Hence please construct a common grok pattern for that single log file.
Please check out this example which has been done on grokconstructor website
Related
I am working on an Azure Data Factory solution to solve the following scenario:
Data files in CSV format are dumped into Data Lake Gen 2 paths. There are two varieties of files, let's call them TypeA and TypeB and each is dumped into a path reflecting a grouping of sensors and the date.
For example:
/mycontainer/csv/Group1-20210729-1130/TypeA.csv
/mycontainer/csv/Group1-20210729-1130/TypeB.csv
/mycontainer/csv/Group1-20210729-1138/TypeA.csv
/mycontainer/csv/Group1-20210729-1138/TypeB.csv
I need to extract data from TypeA files in Delta format into a different location on Data Lake Gen 2 storage. I'll need to do similar processing for TypeB files but they'll have a different format.
I have successfully put together a "Data Flow" which, given a specific blob path, accomplishes step 2. But I am struggling to put together a pipeline which applies this for each file which comes in.
My first thought was to do this based on a storage event trigger, whereby each time a CSV file appeared the pipeline would be run to process that one file. I was almost able to accomplish this using a combination of fileName and folderPath parameters and wildcards. I even had a pipeline which will work when triggered manually (meaning I entered a specific fileName and folderPath value manually). However I had two problems which made me question whether this was the correct approach:
a) I wasn't able to get it to work when triggered by real storage events, I suspect because my combination of parameters and wildcards was ending up including the container name twice in the path it was generating. It's hard to check this because the error message you get doesn't tell you what the various values actually resolve to (!).
b) The cluster that is needed to extract the CSV into parquet Delta and put the results into Data Lake takes several minutes to spin up - not great if working at the file level. (I realize I can mitigate this somewhat - at a cost - by setting a TTL on the cluster.)
So then I abandoned this approach and tried to set up a pipeline which will be triggered periodically, and will pick up all the CSV files matching a particular pattern (e.g. /mycontainer/csv/*/TypeA.csv), process them as a batch, then delete them. At this point I was very surprised to find out that the "Delimited Text" dataset doesn't seem to support wildcards, which is what I was kind of relying on to achieve this in a simple way.
So my questions are:
Am I broadly on the right track with my 'batch of files' approach? Is there a way to define a delimited text data source which reads its data from multiple blobs?
Or do I need a more 'iterative' approach using maybe a 'Foreach' step? I'm really really hoping this isn't the case as it seems an odd pattern to be adopting in 2021.
A much wider question: is ADF a suitable tool for this kind of scenario? I was excited about using it at first, but increasingly it feels like one of those 'exciting to demo but hard to actually use' things which so often pop-up in the low/no code space. Are there popular alternatives which will work nicely with Azure storage?
Any pointers very much appreciated.
I believe you're very much on the right track.
Last week I was able to get wildcard CSV's to be imported if the wildcard is in the CSV name. Maybe create an intermediate step to put all Type A's in the same folder?
Concerning ADF - it's a cool technology, with a steep learning curve (and a lot of updates - incl. breaking changes sometimes) if you're looking to get data ingested without too much coding. Some drawbacks:
Monitoring - if you want to have it cheaper, there's a lot of hacking (e.g. mailing via Logic Apps)
Debugging - as you've noticed, debug messages are often cryptic or insufficient
Multiple monthly updates make it feel like a beta. Indeed, often there are straightforward tasks that are quite difficult to achieve.
Good luck ;)
In Gherkin, you can have free-form text that describes a scenario, a feature, etc. These descriptions are not used by, say, a test runner, but are for you to describe important additional information to another human.
The documentation for Gherkin says that these cannot start a line with one of the other keywords, such as Given, When or Then. Yet, sometimes the best description I could give would be to start with one of these keywords.
I'm sort of making this up as I go here, but here is an example of what I wish I could do:
Scenario: Many notifications at the same time get combined
When we have a lot of notifications being posted at once, it causes problems
for humans. They can't make sense of that much new information all at once.
So if we are ever in a situation where we are posting lots of
notifications in a short time period, we will take the one with the highest
severity and show it with the other notifications as "child" notifications,
accessible via a link that says, "And N other issues."
Given a notification posted today at 11:03:25
And a notification posted today at 11:03:26
And a notification posted today at 11:03:26
And a notification posted today at 11:03:27
When a notification is posted at 11:03.28
Then the notification list will contain 1 notification
And that notification should contain 4 child notifications
The problem I have is that because my description starts with a When, it the tools assume that I've started my specific steps, and blows up on the next line, which doesn't start with a keyword.
I've considered:
Commenting out the first line or the entire description (that seems more consistent to me) but to me, there is a semantic difference between a comment with # and a description.
Rewording the thing to not start with a "When". For example, if it started with, "In times where we have a lot of notifications..." but that's less readable, which is the point with Gherkin-style specifications.
If it wasn't the first word in the whole description, I might be able to get away with simply wrapping my lines differently so that the "When" starts in the middle of a line instead of the beginning, but in this case, I don't have that option.
Those options just seem like workarounds that feel sub-optimal.
Is there a way to "escape" these keywords to tell the system that some usage of "When" is really still just part of the description and not a keyword? If not, is there some sort of accepted best practice or guideline for how people should handle situations like this?
You could use # in the beginning of the line (it's used for writing comments).
Ex:
# When a notification is posted...
You are misinterpretting the the language spec. You can describe a feature and use keywords at the beginning of a line. The example you posted gets interpreted as steps in a scenario since the description appears after the scenario keyword.
Just as Mr Cas said in his answer, you need comments.
Feature: Given a feature title
When I use keywords up here
Then it is allowed
Scenario: When I use keywords after the title to describe a scenario
# Then I need to use comments
I am going to attend the Instance Matching of OAEI, now I need to make my results to Alignment Format. In order to achieve it, I have learned official tutorials.(link:http://alignapi.gforge.inria.fr/tutorial/tutorial1/index.html).
But there are many differences between the method taught and the method I want. In other words, I can't understand the API.
This is my situation:
I have 2 rdf file(person11.rdf and person12.rdf respectively.data link is http://oaei.ontologymatching.org/2010/im/index.html, the PR dataset), each file has information of many person. I want to find the coreferent entities, the results must be printed in Alignment Format. I find the results by using SPARQL, but I don't know how to print it in Alignment Format.
So, I have three questions:
First, if I want to generate a Alignment Format file, is the method taught the only way?
Second, can you give me your method(code better) to generate the Alignment Format file? Maybe I am wrong from the beginning, can you give me some suggestions?
Third, if you attended OAEI or know something about Instance Matching, can you give me some advice? I want to find the coreferent entities.
Thank you!
First question: I guess that the "mentioned method" is the one in tutorial1. It is not the appropriate one since you have to write a program to output the alignment format and this is a command line interface tutorial. In this case, you'd better look at http://alignapi.gforge.inria.fr/tutorial/tutorial2/index.html
Then, there are basically two ways to do:
The advised one (for several reasons and for participating to OAEI) is to follow these tutorials, to create an empty alignment in it, to create the correspondences from the results of your SPARQL query and to render it. Everything is covered by the tutorials but the part concerning your SPARQL queries. This assumes that you are programming in Java.
The non-advised solution (primarily non advised because you will have to debug your own renderer), is to write, in any programming language that you want a program that output the format (which corresponds to what you cite).
Think about it: how would you expect that the Alignment API knows the results of your SPARQL query? If you come up with a nice solution, contact the API developers, they may integrate it and others could benefit.
Second question: I cannot do better than what is above.
Third question: too general. Read the OAEI results (http://oaei.ontologymatching.org) and look at the code of others.
Good luck!
I have a requirement for which I want to trigger an action (like calling a REST-ful service) in the event a keyword is found in the logs. The trigger would have to be fairly real time. I was evaluating open source solutions like GrayLog2, ELK stack (which I believe can't analyse real time), fluentd etc. but wanted to know your opinion on that. It would be great if the tool also allows setting up rules against key words to eliminate false positives and easy to set up.
I hope this makes sense and apologies if this has been discussed elsewhere!
You can try Massalyzer. It's a real-time analyzer too, very fast (up to 10 millinon line per sec), and you can analyze unlimited size with free demo version.
So, I tried Logstash+Graylog2 combination for the scenario I described in the question and it works quite well. I had to tweak a few things to make Logstash work with Graylog2, especially around capturing the right log levels. I will try this out on a highly loaded clustered environment and update my findings here.
I'm new to elasticsearch.The doc on official site just say the basic and do not contain specific example.Due to it is a little disorganized as my view, I can't figure out how to get start to achieve my purpose.
I have crawl a lot of torrents, they are published by many different language.
I see there is analysis in elasticsearch to deal with input text, but I don't understand the work flow. elasticsearch do not use all analyzers to process input data as I try.
It seems I should appoint a analyzer to process a text.
Such as a text :no game no life 游戏人生 ノーゲーム・ノーライフ, it contain three language.How can I know which three analyzers I have to use?And it also too heavy to use all analyzer to process this text.
I have seen a article Three Principles for Multilingal Indexing in Elasticsearch talk about this.However I am a beginner and non-native English speaker, it is hard to understand without a example.
Please give me some guide.
Thank you.
I would probably create two fields (or multiple for number of expected languages) and apply different analyzers (language dependent) to each of them. Then when you search you would search both fields.