Whether you're learning ElasticSearch or you're using it on the daily basis these tools are indispensable. Copying query as an CURL command is really handy if you want to ask question on Stack Overflow. Elastic HQ allows you to see cluster status, cluster statistics, view all nodes that form cluster. You can install it as an ElasticSearch plugin, host it on your own web server or  use cloud version, just visit the link, it's working locally in your browser. I've heard a lot about ElasticSearch lately, was trying to create some time to get a lab set up for the new trio on the block : ELK.
ELK stack is continuing tradition LAMP stack created a while ago by tightly integrating to each other, albeit on a completely different dimension, and becoming new invaluable tool for DevOps people. One of the most important advices I can give to anyone who is building, maintaining or operating an IT infrastructure is having situational awareness on every single angle possible. I won't be going into details of setting up ElasticSearch and Kibana, as there are a lot of blog posts on the net on how to perform those steps.
For the sake of performance and analytics I'm after, I'm only interested in timestamp, source IP, source port, destination IP, destination port, bytes, packets, interface and protocol. After having all 3 files dumped into respective CSV files, I've crafted a Python script utilizing ElasticSearch's official Python API to index each flow record in ElasticSearch. For those who are not familiar with ElasticSearch's mapping definition, here is a short definition of what this schema does.
After this map is PUT on ElasticSearch, we can have our Python script to import CSV file created on step 1.
Please change 'source' and '_type' fields above to reflect file names and "_type" in ElasticSearch index. Once we started zooming into the timeframe when the anomaly occured, we can see all other graphs updated according to the window we selected. When I click 12201 on the destination port pie chart, Kibana re filters and re graphs data according to the selection I made.
I've also captured some information on how much data is required for NetFlow storage when different file formats are in use. I'll continue experimenting with ElasticSearch and post my notes about using ElasticSearch for Netflow analytic purposes. Monitoring the health of an OBIEE system and diagnosing problems that may occur is a vital task for the system’s administrator and support staff.
The ELK stack enables you to rapidly access both summary and detail information across the stack, supporting swift identification and diagnosis of any issues that may occur. Out of the box, OBIEE ships with Enterprise Manager 11g Fusion Middleware Control (FMC), which as the name says is part of the Enterprise Manager line of tools from Oracle for managing systems. There have been some errors, and the top three nQS and ORA error codes and messages are shown. At this point we might want to drill down into what was being run when the errors were being thrown. Another way of diagnosing a sudden rash of errors would be to instead drilldown on time alone to take a more holistic view at the logs (useful also given that ECIDs don’t always give the full picture). Taking a step back up, we can see at a glance which areas of the OBIEE metadata model (RPD) are being used, as well as where we are pulling logs from – and all of these are clickable in order to filter the results further.
At a very high level, we collect and enrich diagnostic data from log files using logstash, store it in ElasticSearch, and present and analyse it through Kibana.
ElasticSearch is a document store, in which data with no predefined structure can be stored. Logstash is an innocuous looking tool that at first glance one could mistakenly write off as “just” a log parser. The final piece of the stack is Kibana, a web application that enables one to build very flexible and interactive time-based dashboards, sourcing data from ElasticSearch. In this article I’m going to show how to set up your own ELK stack to monitor OBIEE, based on SampleApp v406. As you will see below, setting up and configuring the ELK stack does involve rolling up ones sleeves and diving right in. It’s shutdown by default and that’s fine because we need to update the configuration on it anyway. There’s no repository for logstash, but it’s no biggie because there’s no install as such, just a download and unpack. Lastly, Kibana needs to know where to find ElasticSearch, which is where it is going to pull its data from. You should be able to now point your web browser at the server and see the default Kibana dashboard. Now that we’ve got the software installed, let’s see how it hangs together and create our first end-to-end example. Logstash works by reading a configuration file and then running continually waiting for the configured input. It’s pretty obvious what it’s saying – for the input, use stdin, and send it as output to elasticsearch (which will default to the localhost). ElasticSearch can be queried using HTTP requests, and kopf gives a nice way to construct these and see the results which are in JSON format. Now let’s get some proper data in, by pointing logstash at the BI Server log (nqsserver.log).

So our grok will use the pre-defined pattern TIMESTAMP_ISO8601, and then everything else (“GREEDYDATA”) after the timestamp, map to the log message field.
Just to spare you long minutes of consternation: Kibana stores dashboard definitions as documents in ElasticSearch index (it will create index automatically). If you have pie chart of  the Top 20 best sellers and a sales graph (sales over time) then if you select some product on products pie chart it will add filter to your query and sales graph will be showing sales of the selected product. For those who hasn't heard about the term ELK, it is an acronym for ElasticSearch + Logstash and Kibana. ElasticSearch provides schemaless storage and indexing, however just throwing Netflow data without providing a mapping (a schema or DDL some sort) is not smart for storage perspective. First, I didn't want to store NetFlow records in both index and in "_source" field as a JSON document, so it is disabled.
Uploading total of 47 million rows, in 3 different CSF files took about 2 hours on my i7-3770 (QC 3.4GHz CPU).
Once you have uploaded dashboard schema, you'll have something similar to the image on the right.
In the mid section of the dashboard I have source IP, source address, destination IP and destination address pie charts, showing flow itself.
I immediately can see that, TCP traffic nearly diminished, and only UDP traffic is hitting port 12201, which happened to be the GrayLog server's default port listening for logs send by the various app servers.
If I want, I can also drill down to the Interfaces and see how much traffic passed through each interface on the switch. JSON, obviously, being one of the least storage efficient file formats requires most storage when it comes to NetFlow data with 46 millon flows.
It’s one that at Rittman Mead we help customers with implementing themselves, and also provide as a managed service. The responsive interface lets you to drill into time periods or any ad-hoc field or filter as you wish, to analyse and diagnose problems. It is more of a configuration and deployment tool than it is really a monitoring and diagnostics one. This is an important differentiator to EM where you can search for errors, but cannot see straightaway if it is a one-off or multiple occurence. For example, from the error summary alone we can see the biggest problem was a locked database account – but which database was being queried? Using the system activity timeline along with the events log view it is a piece of cake to do this – simply click and drag a time window on the chart to instantly zoom into it. Its origins and core strength are in full text search of any of the data held within it, and it is this that differentiates it from pure document stores such as MongoDB that Mark Rittman wrote about recently. It does a lot more than that and a healthy ecosystem of input, filter, codec and output plugins means that it can interface between a great variety of applications, shifting data from one to another and optionally processing and enriching it along the way. If you’re looking for an off-the-shelf monitoring solution then you should look elsewhere (such as EM12c). The only prerequisite is a JDK for ElasticSearch and Logstash, and web server for Kibana; here I am using Apache. There’s also a wee bit of configuration to do, so that the web server (Apache, in our case) knows to talk to it, and so that Kibana knows how to find ElasticSearch.
An important point here is that the URL of ElasticSearch must be resolvable and accessing from the web browser you run Kibana on, so if you are using a DNS name it must resolve etc.
As well as the input we configure an output, and optionally in between we can have a set of filters.
Notice how you have a histogram of event rates over the past day at the top, and then details of each event at the bottom. By default it’ll chuck every line of the log to ElasticSearch, with the current timestamp – rather than the timestamp of the actual event. A grok is one of the most important of the numerous filter plugins that are available in logstash.
Combined with all mighty ElasticSerach REST API it does whatever you can think of, though your productivity will be impeded. It completes HTTP verb names, urls, endpoint names, query fields, types and type field's names. Main focus of this tutorial is to show how ES and Kibana can be a valuable tool in assessing issues at network layer using Netflow on a real life scenario: On February 4th, 2014 a network issue caused 1 hour disruption to the services provied to a customer, an RCA requested by management. Before importing CSV into ElasticSearch, I've experimented with different schemas, one of which using IP mapping for source and destination IP addresses, however it didn't work well for some reason.
For drilling down and analytics purposes, complete document, NetFlow record in this case, is rarely needed. Most of the time spent was on Python parsing CSV file and converting into JSON format, I didn't have time to optimize code or profile it for performance tuning. With my ElasticSearch Index, dashboard shows high level information about 46 million flows, accounting for 5.8TB of transferred data in 24 hours. This information showed us that the root cause of the issue we were investigating, was actually app servers pumping huge amounts of logs towards GrayLog server.
After disabling "_all" and "_source" fields in ElasticSearch, its storage requirements also went down.
In this article I am going to discuss the ELK stack, which fills a specific gap between the high-level monitoring and configuration functionality of Enterprise Manager 11g Fusion Middleware Control, and the Enterprise-grade monitoring, alerting and configuration management of Enterprise Manager 12c Cloud Control. Data can be summarised and grouped arbitrarily, displaying relative error rates ensuring that genuine problems are not lost in the ‘noise’ of usual operation.

The next step up is FMC’s (very) big brother, Enterprise Manager 12c Cloud Control (EM12c). Or to quickly access all the log files for a specific set of components alone (for example, BI Server and OPMN). Data is loaded and retrieved from ElasticSearch through messages sent over the HTTP protocol, and one of the applications that can send data this way and works extremely well is Logstash. But if you want to have a crack at it I think you’ll be pleasantly surprised at what is possible once you get past the initially (bumpy) learning curve.
Note that if you’re doing anything funky with network, your local web browser needs to be able to hit both Apache (port 80 by default), and ElasticSearch (port 9200 by default).
Here, I’ll just look at some of the very basics, creating a very simple logstash configuration which will prompt for input (i.e. What’s actually happened is that the text, plus some information such as the current timestamp, has been sent to ElasticSearch.
9200 is the port on which ElasticSearch listens by default, and kopf is a plugin we can use to inspect ElasticSearch’s state and data. Grok statement are written as Regular expressions, or regex (obXKCD), so to avoid continual wheel-reinventing of regex statements for common objects (time, ip addresses, etc) logstash ships with a bunch of these predefined, and you can build your own too. All logs gathered in one place, with very few reliable explanation as to what really happened. Probing outside interfaces means recording traffic from spoofed IPs, ICMP pings, BGP announcements and every other bits that travels on Layer 3. I've used SSD drive to store ElasticSearch data files, which makes upload and analytics faster than traditional drives.
Whole issue was triggered by another issue, but it was start of chain reaction, causing apps to go crazy with logs, making log pumping the root cause, and trigger itself a contributing factor.
900MB of gzip compressed Nfdump data consumes about 3.8 GB of Index space on ElasticSearch. This is very much its own product, requiring its own infrastructure and geared up to monitoring an organisation’s entire fleet of [Oracle] hardware and software. Any field that is displayed, whether in a chart or a detailed log view, can be clicked and used as the input for an ad-hoc filter.
The capabilities are great, and there’s an active support community as is the case with lots of open-source tools. ElasticSeach has a concept of an index, in which documents, maybe of the same repeating structure but not necessarily, can be stored. First up, go and enter a bit more data into logstash, so that the create events have been spread out over time. So everything in a log message, such as the timestamp, user, ecid, and so on – all can be extracted from the input and stored as distinct items. You get a lot of analytic around cluster, nodes and indices state in one place (also about local configuration of each node).
You can have multiple queries in one window, you can run them by clicking green arrow next to the endpoint address.
Also after inserting 47 million rows, into 3 different types, there will be a lot of segments in ElasticSearch index directories. I should add that, I didn't store all Netflow fields in this test scenario, only included ones that are relevant to my use case.
Acutely your are converting as CSV file then feed data to elastic search, It's possible feed to live data. With a bit of work it is possible to create a monitoring environment tailored pretty much entirely to your design. The kopf plugin that we installed above can show that the data made it to ElasticSeach, and finally we will create a very simple Kibana dashboard to demonstrate its use. Crudely put, this can be seen as roughly analogous to tables and rows of data respectively. Click the refresh icon on the Kibana dashboard, and then click-drag to select just the period on the chart that has data. They can also be used for further processing – such as amending the timestamp output from the logstash event to that of the log file line, rather than the system time at which it was processed. ES & Kibana helped a lot to better understand and grasp what happened during the disruption and nailing down root cause. To make comparison a little more accurate, I've also added uncompressed Nfdump storage requirements below.
The ELK stack conceptually fits perfectly alongside your existing EM FMC, providing a most excellent OBIEE monitoring dashboard and analysis tool, and allowing you to explore the kind of diagnostics and historical data that you could have access to in EM 12c. I chose 'not_analyzed' for string fields, as there is no need to tokenize or stem any of the strings stored.
Go and click on one of the event messages in the lower pane and see how it expands, showing the value of each field – including message which is what logstash sent through from its input to output. This tells me that I can also store same Index files on ZFS with LZ compression turned on to save some space without sacrificing too much performance.
Please duplicate above mapping for each of the NetFlow collectors after changing "fnf1x" to the appropriate name you choose.

Power drill screwdriver bits 130
Tool kit for honda shadow

Comments to «Kibana drill down»

  1. QaRa_BaLa writes:
    Switch and speed adjustment cash for the acquire of other a lot more vital.
  2. 10 writes:
    Are functioning on specially with blades, metal and.

2015 Electrical hand tool set organizer | Powered by WordPress