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© COPYRIGHT PROS, INC. 2013 | CONFIDENTIAL AND PROPRIETARY
Night OwlLog Monitoring using Elasticsearch and Hadoop
Boyd Meier ([email protected])Hadoop Meetup – October 16, 2013
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Problem
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Application Performance Monitoring
● Many servers● Many applications● Many log formats● Many places to go look for information
● What if we could just look in one place and see everything?
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Advanced Analysis
● The logs are too low-level● The servers need the existing capacity● The amount of data to be analyzed is huge● Some analysis needs to be across multiple servers● What if we want to change the analysis algorithms?
● How we can do analysis in the most flexible way possible?
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Proactive Support
● See problems coming before they become crises● Watch for errors and exceptions● Track performance of the application● Track usage of the application● Enable checks we haven’t thought of yet
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Some Analysis Questions
● What errors happen, and how often?● Who did what, when?● How long did it take to do a task?● What else was happening on the server?
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Constraints
● Very little budget – as much free stuff as possible● Can’t use client machines● Communications need to be secure● Large amounts of data (Gb/day/client)● Minimize support’s dependence on client IT
Approach
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Hadoop
● We have a lot of data (~2 GB day with 3 clients)● We need to process it in reasonable time● We can’t afford a big machine for this● We have lots of old machines lying around
● Sounds like a job for the elephant! But what about query?
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Elasticsearch
● Query performance on base Hadoop is painful● Ad-hoc queries are required● Hadoop integration● Cluster deployment
● Looks promising! How do we get the data into the server?
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Logstash
● Handle many sources, not just logs● Fan-in architecture to server● Compressed, SSL encrypted data● Can offload some logic on the client if desired● Massively configurable● Output to Elasticsearch
● Great! Now how about visualization?
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Kibana
● Backed by Elasticsearch● Supports dynamic queries● View information over time● Built-in support for Logstash ● Configurable, shareable dashboards
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High Level Software Architecture
KibanaElasticsearch
LogstashClient
LogstashServer
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Hadoop Processing
● Pig scripts process the data● Wonderdog from InfoChimps to integrate Pig and
Elasticsearch– There are issues:• Cluster stability using Wonderdog• Wonderdog Pig interface has not been updated in a while• Currently evaluating elasticsearch-hadoop project from Elasticsearch.org
● Analysis results are stored in Elasticsearch for ease of access
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Demo
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Configuration Details
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Software
● Ubuntu 12.04.2 LTS (Precise)● Cloudera CDH 4.3.1–Hadoop 2.0.0–Hbase 0.94–Hive 0.10– Pig 0.11
● Elasticsearch 0.90.3● Logstash 1.1.12● Kibana 3 M3
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Hardware Architecture
● 27 node cluster of commodity machines ● 42 TB of disk space● Connected via 10 gigabit switch● Each machine has:– 8 GB RAM– 2 TB SATA HDD–Gigabit Ethernet
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Performance
● Over the month of September:– 188 million events ingested from 3 clients– 57.5 GB storage used (1.92 GB / day)
● At that rate, 42 TB is enough space for:– 142 billion events– 60 years of data from these clients– 1 year of data from 180 clients at the same volume per client
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Resources
● Elasticsearch - http://www.elasticsearch.org/overview/• http://github.com/elasticsearch/elasticsearch
● Logstash - http://www.elasticsearch.org/overview/logstash/• https://github.com/logstash/logstash
● Kibana - http://www.elasticsearch.org/overview/kibana/• https://github.com/elasticsearch/kibana
● ES – Hadoop - http://www.elasticsearch.org/overview/hadoop/• http://github.com/elasticsearch/elasticsearch-hadoop
● Cloudera - http://www.cloudera.com/
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