sinhan · June 4, 2017 23:57 · Jun 4, 2017 · Jun 4, 2017 · Jun 4, 2017 · Jun 4, 2017
diff --git a/gistfile1.txt b/gistfile1.txt
@@ -88,7 +88,7 @@ and frontend servers)
 
 	- Load Balancer : Session utilization, latency, Denials, queue length and queue time
 	- Oracle : Number of connections to database
-	- MySQL : 
+	- MySQL : Read/Write requests,Uptime,Threads_connected,Max_used_connections,Aborted_connects,InnoDB deadlocks,slow query log, salve lag, full table scans
 
 3. Monitor website :
 	- Whitebox monitoring : Monitoring based on metrics exposed by the internals of the system like 

diff --git a/gistfile1.txt b/gistfile1.txt
@@ -50,6 +50,58 @@ Monitoring : observation, checking and recording
 
 In containerized world : label data at source. Push and parse as soon as possible
 
+=========================================
+
+.Monitor individual servers/VMS - Sutained CPU utlization ,Load per CPU, Memory consumptions, HEAP, NTP offsets, Disc usage, Active connections, 
+
+network traffic , swap usage: Using Nagios
+
+2.Monitor applications : Using Shell scripting, nagios/Sensu, Log Aggregator, Splunk , Code instrumentation (byteman for tomcat), verifying 
+
+server process are up
+
+	- Apache : Logs , mod_status, (number of incoming requests, CPU Usage, Server load, server uptime,total tarffic, worker pool, idle vs 
+
+active connections, number of threads)
+	- RabbitMq : Logs,rabbitmqctl,/management/nagios plugin, number of message in a queue, acknowledged vs unack, queue timeouts, cpu/memory 
+
+usage, messages in queue, publish rate, get rate, health status, open sockets, open files
+	- NginX : Logs, access_log directive,ngxtop HttpStubStatusModule module,serverdensity ,: requests per second , number of connections, 
+
+baseline traffic, uptime,  CPU overload
+	- NodeJs server : Logs, response time of imporatnt services/apis/webpages, transaction errors, Free, Used, and Max Heap, Non-Heap Memory, 
+
+Garbage collection, Total time spent actively executing in each event loop tick, Event loop ticks per minute
+	- Redis : Log, redis-cli, memory, max concurrent connections, cache hit ratio, evictions, expired objects
+	- Tomcat : Logs	, enable JMX, JVM Heap an,d memory utilization, thread usage, request throughput, sessions,threadpool, GC Collection, 
+
+Thread pool, Active threads,Active, expired and rejected HTTP session 
+	- Code : Instrumentation for trace
+	- Jboss : Same as above tomcat
+	- HAProxy : properly distributing traffic, Error Rate (per-min), Proxy Status, Request Rate (per-min), Active Servers, Sessions Active, 
+
+Sessions Queued, frontend metrics, backend metrics, health metrics, session utilization, http client and server errors, average backend response 
+
+time, number of time connection retries, , connection failures, response denied or failed,q time, ( check directive will detect health of backend 
+
+and frontend servers)
+
+	- Load Balancer : Session utilization, latency, Denials, queue length and queue time
+	- Oracle : Number of connections to database
+	- MySQL : 
+
+3. Monitor website :
+	- Whitebox monitoring : Monitoring based on metrics exposed by the internals of the system like 
+		http responses and error codes,
+		Response time for services, apis, webpages
+		Drops/Spikes for different pages with differnt pages : Browse, Search, products, Add to card, checkout
+		Drops and spike for aggregation from DB : OPM
+	- Blackbox monitoring : Testing externally visible behavior as a user would see it. user journey .Most important
+		Page load performance
+		Page view throughput
+		Browser traces
+	- Ping Tests : Geographic performance,
+
 
 
 

diff --git a/gistfile1.txt b/gistfile1.txt
@@ -19,9 +19,37 @@ With a pull-based approach, your monitoring system needs to know which service i
 To get high availability, pull allows you to just run two identically configured Prometheus servers in parallel
 Whether you pull or push, any time-series database will fall over if you send it more samples than it can handle. 
 
-EFK
+EFK (Elasticsearch, Fluentd, and Kibana)
+both Fluentd and Logstash provide both log forwarders and log shippers 
+Log shippers are an essential component in modern devops, because logs are streams, not files 
+Log forwarders send logging events to log shippers. A forwarder's goal is to send those events upstream as quickly as possible. Log shippers make delivery/routing decisions based upon the log event stream. A shipper may aggregate events, and/or send them to remote storage or analysis tools. 
 
 
+Both Kibana and Grafana are powerful visualization tools. However, the Grafana and InfluxDB combination is used for metric data whereas Kibana is part of the popular ELK Stack, which provides more flexibility when exploring log data.
+Both platforms are good options and can even sometimes complement each other. First, use Kibana to analyze your logs. Then, export the data into Grafana as the visualization layer. Both rely on the same Elasticsearch repository.
+
+
+
+Generate - > Collect -> Transpor - > Store - > Analyze - > Alerts
+Generate : Application logs, syslog,  proxy and web servers, 
+Logging considerations ● Logging means more code ● Logging is not free ● Consider feedback to the UI instead of logging ● The more you log, the less you can find ● Consider to log only the most evil scenarios (log exceptions) ● Agree on levels like FATAL, ERROR, WARN, DEBUG, INFO, TRACE 
+
+Collect : Stdout, files,
+Transport : transporters and collectors, Logstash, Flume, Fluent . Pull vs push for traffic
+
+Store : Short vs long, speed of data ingestion ad retrieval , data access,.  Can be S3, Elastic Search, Cassandra, HBASE,
+
+Analyze : 
+	Batch processing -  HDFS, Hive, Pig -> Map Reduce
+	UI based : Kibana, Garylog
+Alerts : Based on patterns or calculated metric  send out events.
+
+Logging is not monitoring
+Logging : recording to diagnose system
+Monitoring : observation, checking and recording
+
+In containerized world : label data at source. Push and parse as soon as possible
+
 
 
 

diff --git a/gistfile1.txt b/gistfile1.txt
@@ -0,0 +1,28 @@
+OpenTSDB:
+Scales well, keep all data, creating metric is easy : OpnenTSDB ( vs Ganglia). 
+Anamoly detection : Skyline and oculus by etsy. Icinga checks for metrics
+Opensource and distributed based on HBASE/HDFS
+tcollector framework to colect and put data
+decouple mesaurment from storage
+Very precise and collects trillion of data points. never lose precision
+Very good for IOT, distributed systems
+Monitor : Application performance, network performance, resource utilization,
+FrontEnds: By box, tcketmaster,
+How to guard against chatty service . It difficult. Promethius is better ( digital ocen)
+
+Promethius + Garphana
+Add Vulacan
+Pull based just like nagios but only collects time series data
+Uses TCP for pull rather than UDP for push
+Not a event based monitoring system nor it stores raw data
+With a pull-based approach, your monitoring system needs to know which service instances exist and how to connect to them
+To get high availability, pull allows you to just run two identically configured Prometheus servers in parallel
+Whether you pull or push, any time-series database will fall over if you send it more samples than it can handle. 
+
+EFK
+
+
+
+
+
+
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