Log management with ELK

Oct 7, 2014Download as PPTX, PDF7 likes3,278 views

ELK (Elasticsearch, Logstash, Kibana) is an open source toolset for centralized logging, where Logstash collects, parses, and filters logs, Elasticsearch stores and indexes logs for search, and Kibana visualizes logs. Logstash processes logs through an input, filter, output pipeline using plugins. It can interpret various log formats and event types. Elasticsearch allows real-time search and scaling through replication/sharding. Kibana provides browser-based dashboards and visualization of Elasticsearch query results.

Log Management with ELK
ELASTICSEARCH, LOGSTASH, KIBANA FOR CENTRALIZED LOGS

Purpose
Centralized Log Management
◦ Collect, Parse and Filter using Logstash
◦ Store, Index and Search using Elasticsearch
◦ Visualize using Kibana
Full open source stack
◦ Use for free
◦ Support plan from Elasticsearch company

Elasticsearch
Real-time search engine
◦ Based on Apache Solr/Lucene
◦ Pure Java
◦ Document database
◦ Advanced text indexing
◦ Fuzzy search
◦ Replication/Sharding for true scalability

Logstash
JRuby Based log processor
Pluggable event pipeline
◦ Input plugins
◦ Filter plugins
◦ Codec plugins
◦ Output plugins
DevOps Comunity
◦ Mix of developers, operations and system administrators

Kibana
Browser based dashboard for ElasticSearch
Visualization of query results
◦ Time Charts
◦ Filter any field
◦ Compare subsets

Logstash pipeline
Define input, filters and outputs
Simple configuration file
Ruby syntax

Logstash not just for logs
Interpretes different log formats
◦ Syslog messages
◦ Log4j with full details
◦ Apache log files
Other event types too
◦ Ganglia server monitoring events
◦ SNMP events
◦ Windows EventLog
Pre-proces before sending
◦ lumberjack

Logstash Inputs
Rsyslog via TCP/UDP
Log4j appender
JMX Listener
Logstash-forwarder
File tails
SNMP
Ganglia
…

Grok Filter
Readable regex
Predefined patterns for common log data
Extract to properties
◦ Indexed properties

Metrics Filter
Aggregate metrics
◦ Event rate using sliding windows
◦ 1 min
◦ 5 min
◦ 15 min
◦ Min/max/stddev/percentiles

Statsd output
Node.js based
◦ Counters
◦ Timers
◦ Graphite frontend

Alert outputs
Send alerts
◦ Email
◦ Pagerduty
◦ XMPP/Jabber
◦ Hipchat
◦ Nagios
Use treshold from metrics filter

ElasticSearch output
Auto-creates new index per day
Index all recognized fields
Full text index, customizable indexer, mapper

Scalability
Easy deployment using chef/puppet/docker

The document introduces the ELK stack, which consists of Elasticsearch, Logstash, Kibana, and Beats. Beats ship log and operational data to Elasticsearch. Logstash ingests, transforms, and sends data to Elasticsearch. Elasticsearch stores and indexes the data. Kibana allows users to visualize and interact with data stored in Elasticsearch. The document provides descriptions of each component and their roles. It also includes configuration examples and demonstrates how to access Elasticsearch via REST.

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Centralized log management is implemented using the Elastic Stack including Filebeat, Logstash, Elasticsearch, and Kibana. Filebeat ships logs to Logstash which transforms and indexes the data into Elasticsearch. Logs can then be queried and visualized in Kibana. For large volumes of logs, Kafka may be used as a buffer between the shipper and indexer. Backups are performed using Elasticsearch snapshots to a shared file system or cloud storage. Logs are indexed into time-based indices and a cron job deletes old indices to control storage usage.

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La gestione dei log è da sempre un argomento complesso e nel tempo si sono cercate varie soluzioni più o meno complesse, spesso difficili da integrare nel proprio stack applicativo. Daremo un’ overview generale dei principali sistemi di aggregazione evoluta dei log in realtime (Fluentd, Greylog, eccetera) e illustreremo del motivo ci ha spinto a scegliere ELK per risolvere un’esigenza del nostro cliente; ovvero di consultare i log in modo piu comprensibile da persone non tecniche. Lo stack ELK (Elasticsearch Logstash Kibana) permette agli sviluppatori di consultare i log in fase di debug / produzione senza avvalersi dello staff sistemistico. Dimostreremo come abbiamo eseguito il deployment dello stack ELK e lo abbiamo implementato per interpretare e strutturare i log applicativi di Magento.

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Log management with ELK

1. Log Management with ELK ELASTICSEARCH, LOGSTASH, KIBANA FOR CENTRALIZED LOGS

2. Purpose Centralized Log Management ◦ Collect, Parse and Filter using Logstash ◦ Store, Index and Search using Elasticsearch ◦ Visualize using Kibana Full open source stack ◦ Use for free ◦ Support plan from Elasticsearch company

3. Elasticsearch Real-time search engine ◦ Based on Apache Solr/Lucene ◦ Pure Java ◦ Document database ◦ Advanced text indexing ◦ Fuzzy search ◦ Replication/Sharding for true scalability

4. Logstash JRuby Based log processor Pluggable event pipeline ◦ Input plugins ◦ Filter plugins ◦ Codec plugins ◦ Output plugins DevOps Comunity ◦ Mix of developers, operations and system administrators

5. Kibana Browser based dashboard for ElasticSearch Visualization of query results ◦ Time Charts ◦ Filter any field ◦ Compare subsets

6. Logstash pipeline Define input, filters and outputs Simple configuration file Ruby syntax

7. Logstash not just for logs Interpretes different log formats ◦ Syslog messages ◦ Log4j with full details ◦ Apache log files Other event types too ◦ Ganglia server monitoring events ◦ SNMP events ◦ Windows EventLog Pre-proces before sending ◦ lumberjack

8. Logstash Inputs Rsyslog via TCP/UDP Log4j appender JMX Listener Logstash-forwarder File tails SNMP Ganglia …

9. Logstash Filters

10. Grok Filter Readable regex Predefined patterns for common log data Extract to properties ◦ Indexed properties

11. Metrics Filter Aggregate metrics ◦ Event rate using sliding windows ◦ 1 min ◦ 5 min ◦ 15 min ◦ Min/max/stddev/percentiles

12. Logstash Outputs

13. Statsd output Node.js based ◦ Counters ◦ Timers ◦ Graphite frontend

14. Alert outputs Send alerts ◦ Email ◦ Pagerduty ◦ XMPP/Jabber ◦ Hipchat ◦ Nagios Use treshold from metrics filter

15. ElasticSearch output Auto-creates new index per day Index all recognized fields Full text index, customizable indexer, mapper

16. Kibana dashboard

17. Scalability Easy deployment using chef/puppet/docker

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