ELK: Moose-ively scaling your log system

1
ELK: Moose-ively
scaling your log system
Lessons From Etsy’s 3-year Journey with 
Elasticsearch, Logstash and Kibana

Monitoring And
Scaling Logstash
ACT 2
# Beyond Web-Scale:
Moose-Scale
Elasticsearch
ACT 3
#Sizing Up Your
Elasticsearch Cluster
ACT 1
#
Agenda

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REQUIREMENTS FROM YOU:
1. ASK QUESTIONS 
2. DISCUSS THE TOPICS

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HANG IN THERE,
SOME OF THIS IS
A LITTLE DRY!

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PROLOG U E
Etsy’s ELK clusters

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GUESS THE
CLUSTER SIZE:
STORAGE CAPACITY

8
321
Combined cluster sizeNumber of clusters Log lines indexed
Six 300 ES instances
141 physical servers
4200 CPU cores
38Tb RAM
1.5Pb Storage
10 billion/day
Up to 400k/sec
Etsy’s ELK clusters

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Healthy Advice
• Rename your cluster from “elasticsearch” to something else. 
When you end up with two Elasticsearch clusters on your network, you’ll
be glad you did.
• Oops, deleted all the indices again! 
Set action.destructive_requires_name=true
• Always use SSDs. This is not optional.
• If you’re seeing this talk, you probably need 10G networking too.
• Use curator. We developed our own version before it was available.

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ACT 1, SC ENE 1
Sizing up your Elasticsearch
Cluster

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What resources inﬂuence cluster make-up?
• CPU
- Cores > clock speed
• Memory
- Number of documents
- Number of shards
• Disk I/O
- SSD sustained write rates
• Network bandwidth
- 10G mandatory on large installations for fast recovery / relocation

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What resources inﬂuence cluster memory?
• Memory
- Segment memory: ~4b RAM per document = ~4Gb per billion log lines
- Field data memory: Approximately same as segment memory 
(less for older, less accessed data)
- Filter cache: ~1/4 to 1/2 of segment memory, depending on searches
- All the rest (at least 50% of system memory) for OS file cache
- You can't have enough memory!

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What resources inﬂuence cluster I/O?
• Disk I/O
- SSD sustained write rates
- Calculate shard recovery speed if one node fails:
- Shard size = (Daily storage / number of shards)
- (Shards per node * shard size) / (disk write speed / shards per node)
• Eg: 30Gb shards, 2 shards per node, 250Mbps write speed:
- (2 * 30Gb) / 125Mbps = 8 minutes
• How long are you comfortable losing resilience?
• How many nodes are you comfortable losing?
• Multiple nodes per server increase recovery time

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What resources inﬂuence cluster networking?
• Network bandwidth
- 10G mandatory on large installations for fast recovery / relocation
- 10 minute recovery vs 50+ minute recovery:
• 1G Bottleneck: Network uplink
• 10G Bottleneck: Disk speed

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ACT 1, SC ENE 2
Sizing up your Logstash
Cluster

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Sizing Up Your Logstash Cluster: Resources
CPU

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Sizing Up Logstash: CPU
• Rule 1: Buy as many of the fastest CPU cores as you can aﬀord
• Rule 2: See rule 1
• More filtering == more CPU

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WE'LL RETURN TO
LOGSTASH CPU SHORTLY!
BUT FIRST…

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• Easy to use
• Data saved to ES
• So many metrics!
• No integration
• Costs $$$
• Time to develop
• Integrates with your
systems
• Re-inventing the wheel
• Free (libre, not gratis)
Roll your ownMarvel

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Monitoring: Elasticsearch
• Metrics are exposed in several places:
- _cat API 
Covers most metrics, human readable
- _stats API, _nodes API 
Covers everything, JSON, easy to parse
• Send to Graphite
• Create dashboards

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Monitoring: Systems
• SSD endurance
• Monitor how often Logstash says the pipeline is blocked 
If it happens frequently, find out why (mention the possibilities and that
we’ll cover them later)

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Monitoring: Systems
• Dynamic disk space thresholds
• ((num_servers - failure_capacity) / num_servers) - 15%
- 100 servers
- Allow up to 6 to fail
- Disk space alert threshold = ((100 - 6) / 100) - 15%  
Disk space alert threshold = 79%
• Let your configuration management system tune this up and down for
you, as you add and remove nodes from your cluster.
• The additional 15% is to give you some extra time to order or build more
nodes.

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ACT 3, SC ENE 1
Scaling Logstash

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Scaling Logstash: What impacts performance?
• Line length
• Grok pattern complexity - regex is slow
• Plugins used
• Garbage collection
- Increase heap size
• Hyperthreading
- Measure, then turn it oﬀ

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Scaling Logstash: Measure Twice
• Writing your logs as JSON has little benefit, unless you do away with
grok, kv, etc. Logstash still has to convert the incoming string to a ruby
hash anyway.

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HOW MUCH DOES
RUBY LOVE
CREATING OBJECTS?

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Scaling Logstash: Garbage Collection
• Defaults are usually OK
• Make sure you’re graphing GC
• Ruby LOVES to generate objects: monitor your GC as you scale
• Write plugins thoughtfully with GC in mind:
- Bad: 1_000_000.times { "This is a string" } 
user system total real 
time 0.130000 0.000000 0.130000 ( 0.132482)
- Good: foo = 'This is a string'; 1_000_000.times { foo } 
user system total real 
time 0.060000 0.000000 0.060000 ( 0.055005)

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Scaling Logstash
Plugin performance

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Scaling Logstash: Plugin Performance: Baseline
• How to establish a baseline
• Measure again with some filters
• Measure again with more filters
• Establish the costs of each filter
• Community filters are for the general case
- You should write their own for your specific case
- Easy to do
• Run all benchmarks for at least 5 mins, with a large data set

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• Establish baseline throughput: Python, StatsD, Graphite
• Simple logstash config, 10m apache log lines, no filtering:
- input { 
file { 
path => "/var/log/httpd/access.log"  
start_position => "beginning"  
} 
} 
output { 
stdout { codec => "dots" } 
}

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• Establish baseline throughput: Python, StatsD, Graphite
• Python script to send logstash throughput to statsd:
- sudo pip install statsd
- #!/usr/bin/env python 
import statsd, sys 
c = statsd.StatsClient('localhost', 8125) 
while True: 
sys.stdin.read(1) 
c.incr('logstash.testing.throughput', rate=0.001)
• Why don't we use the statsd output plugin? It slows down output!

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• Establish baseline throughput
• Tie it all together:
- logstash -f logstash.conf | pv -W | python throughput.py
Garbage 
collection!

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HOW MUCH DID
GROK SLOW DOWN
PROCESSING IN 1.5?

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Scaling Logstash: Plugin Performance: Grok
• Add a simple grok filter
• grok { match => [ "message", "%{ETSY_APACHE_ACCESS}" ] }
• 80% slow down with only 1 worker
Oops! 
Only one filter worker!

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Scaling Logstash: Plugin Performance: Grok
• Add a simple grok filter
• grok { match => [ "message", "%{APACHE_ACCESS}" ] }
• Add: -w <num_cpu_cores>, throughput still drops 33%: 65k/s -> 42k/s
No Grok 
1 worker
1 Grok 
1 worker
1 Grok 
32 workers

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YOUR BASELINE IS THE
MINIMUM AMOUNT OF
WORK YOU NEED TO DO

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Scaling Logstash: Plugin Performance: kv
• Add a kv filter, too: 
kv { field_split => "&" source => "qs" target => "foo" }
• Throughput similar, 10% drop (40k/s)
• Throughput more variable due to heavier GC

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DON’T BE AFRAID
TO REWRITE
PLUGINS!

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Scaling Logstash: Plugin Performance
• kv is slow, we wrote a `splitkv` plugin for query strings, etc:
kvarray = text.split(@field_split).map { |afield|
pairs = afield.split(@value_split)
if pairs[0].nil? || !(pairs[0] =~ /^[0-9]/).nil? || pairs[1].nil? ||
(pairs[0].length < @min_key_length && !@preserve_keys.include?(pairs[0]))
next
end
if !@trimkey.nil?
# 2 if's are faster (0.26s) than gsub (0.33s)
#pairs[0] = pairs[0].slice(1..-1) if pairs[0].start_with?(@trimkey)
#pairs[0].chop! if pairs[0].end_with?(@trimkey)
# BUT! in-place tr is 6% faster than 2 if's (0.52s vs 0.55s)
pairs[0].tr!(@trimkey, '') if pairs[0].start_with?(@trimkey)
end
if !@trimval.nil?
pairs[1].tr!(@trimval, '') if pairs[1].start_with?(@trimval)
end
pairs
}
kvarray.delete_if { |x| x == nil }
return Hash[kvarray]

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SPLITKV LOGSTASH CPU:
BEFORE: 100% BUSY 
AFTER: 33% BUSY

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Scaling Logstash: Elasticsearch Output
• Logstash output settings directly impact CPU on Logstash machines
- Increase flush_size from 500 to 5000, or more.
- Increase idle_flush_time from 1s to 5s
- Increase output workers
- Results vary by log lines - test for yourself:
• Make a change, wait 15 minutes, evaluate
• With the default 500 from logstash, we peaked at 50% CPU on the
logstash cluster, and ~40k log lines/sec. Bumping this to 10k, and
increasing the idle_flush_time from 1s to 5s got us over 150k log lines/
sec at 25% CPU.

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Scaling Logstash: Elasticsearch Output

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Scaling Logstash
Pipeline performance

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…/vendor/…/lib/logstash/pipeline.rb 
Change SizedQueue.new(20) 
to SizedQueue.new(500)
—pipeline-batch-size=500
After
Logstash 2.3
Before
Logstash 2.3
This is best changed at the end of tuning. 
Impacted by output plugin performance.

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Scaling Logstash
Testing conﬁguration changes

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Scaling Logstash: Adding Context
• Discovering pipeline latency
- mutate { add_field =>  
[ "index_time", "%{+YYYY-MM-dd HH:mm:ss Z}" ]  
}
• Which logstash server processed a log line?
- mutate { add_field =>  
[ "logstash_host", "<%= node[:fqdn] %>" ]  
}
• Hash your log lines to enable replaying logs
- Check out the hashid plugin to avoid duplicate lines

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Scaling Logstash: Etsy Plugins
http://github.com/etsy/logstash-plugins

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Scaling Logstash: Adding Context
• ~10% hit from adding context

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Scaling Logstash: Testing Conﬁguration Changes
describe package('logstash'),
:if => os[:family] == 'redhat' do
it { should be_installed }
end
describe command('chef-client') do
its(:exit_status) { should eq 0 }
end
describe command('logstash -t -f ls.conf.test') do
end
describe command('logstash -f ls.conf.test') do
its(:stdout) { should_not match(/parse_fail/) }
end
describe command('restart logstash') do
end
describe command('sleep 15') do
end
describe service('logstash'),
:if => os[:family] == 'redhat' do
it { should be_enabled }
it { should be_running }
end
describe port(5555) do
it { should be_listening }
end

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input {
generator {
lines => [ '<Apache access log>' ]
count => 1
type => "access_log"
}
generator {
lines => [ '<Application log>' ]
count => 1
type => "app_log"
}
}

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filter {
if [type] == "access_log" {
grok {
match => [ "message", "%{APACHE_ACCESS}" ]
tag_on_failure => [ "parse_fail_access_log" ]
}
}
if [type] == "app_log" {
grok {
match => [ "message", "%{APACHE_INFO}" ]
tag_on_failure => [ "parse_fail_app_log" ]
}
}
}

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output {
stdout {
codec => json_lines
}
}

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Scaling Logstash: Summary
• Faster CPUs matter
- CPU cores > CPU clock speed
• Increase pipeline size
• Lots of memory
- 18Gb+ to prevent frequent garbage collection
• Scale horizontally
• Add context to your log lines
• Write your own plugins, share with the world
• Benchmark everything

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ACT 3, SC ENE 2
Scaling Elasticsearch

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Let's establish our baseline

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Scaling Elasticsearch: Baseline with Defaults
• Logstash output: Default options + 4 workers 
Elasticsearch: Default options + 1 shard, no replicas 
We can do better!

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What Impacts Indexing
Performance?

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Scaling Elasticsearch: What impacts indexing performance?
• Line length and analysis, default mapping
• doc_values - required, not a magic fix:
- Uses more CPU time
- Uses more disk space, disk I/O at indexing
- Helps blowing out memory.
- If you start using too much memory for fielddata, look at the biggest
memory hogs and move them to doc_values
• Available network bandwidth for recovery

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• CPU:
- Analysis
- Mapping
• Default mapping creates tons of .raw fields
- doc_values
- Merging
- Recovery

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• Memory:
- Indexing buﬀers
- Garbage collection
- Number of segments and unoptimized indices
• Network:
- Recovery speed
• Translog portion of recovery stalls indexing 
Faster network == shorter stall

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Memory

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Scaling Elasticsearch: Where does memory go?
• Example memory distribution with 32Gb heap:
- Field data: 10% 
Filter cache: 10% 
Index buﬀer: 500Mb
- Segment cache (~4 bytes per doc): 
How many docs can you store per node?
• 32Gb - ( 32G / 10 ) - ( 32G / 10 ) - 500Mb = ~25Gb for segment cache
• 25Gb / 4b = 6.7bn docs across all shards
• 10bn docs / day, 200 shards = 50m docs/shard 
1 daily shard per node: 6.7bn / 50m / 1 = 134 days 
5 daily shards per node: 6.7bn / 50m / 5 = 26 days

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Scaling Elasticsearch: Doc Values
• Doc values help reduce memory
• Doc values cost CPU and storage
- Some fields with doc_values: 
1.7G Aug 11 18:42 logstash-2015.08.07/7/index/_1i4v_Lucene410_0.dvd
- All fields with doc_values: 
106G Aug 13 20:33 logstash-2015.08.12/38/index/_2a9p_Lucene410_0.dvd
• Don't blindly enable Doc Values for every field
- Find your most frequently used fields, and convert them to Doc Values
- curl -s 'http://localhost:9200/_cat/fielddata?v' | less -S

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Scaling Elasticsearch: Doc Values
• Example field data usage: 
 
total request_uri _size owner ip_address 
117.1mb 11.2mb 28.4mb 8.6mb 4.3mb 
96.3mb 7.7mb 19.7mb 9.1mb 4.4mb 
93.7mb 7mb 18.4mb 8.8mb 4.1mb 
139.1mb 11.2mb 27.7mb 13.5mb 6.6mb 
96.8mb 7.8mb 19.1mb 8.8mb 4.4mb 
145.9mb 11.5mb 28.6mb 13.4mb 6.7mb 
95mb 7mb 18.9mb 8.7mb 5.3mb 
122mb 11.8mb 28.4mb 8.9mb 5.7mb 
97.7mb 6.8mb 19.2mb 8.9mb 4.8mb 
88.9mb 7.6mb 18.2mb 8.4mb 4.6mb 
96.5mb 7.7mb 18.3mb 8.8mb 4.7mb 
147.4mb 11.6mb 27.9mb 13.2mb 8.8mb 
146.7mb 10mb 28.7mb 13.6mb 7.2mb

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Scaling Elasticsearch: Memory
• Run instances with 128Gb or 256Gb RAM
• Configure RAM for optimal hardware configuration
- Haswell/Skylake Xeon CPUs have 4 memory channels
• Multiple instances of Elasticsearch
- Do you name your instances by hostname? 
Give each instance it’s own node.name!

70
Scaling Elasticsearch: CPUs
• CPU intensive activities
- Indexing: analysis, merging, compression
- Searching: computations, decompression
• For write-heavy workloads
- Number of CPU cores impacts number of concurrent index operations
- Choose more cores, over higher clock speed

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Scaling Elasticsearch: That Baseline Again…
• Remember our baseline?
• Why was it so slow?

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[logstash-2016.06.15][0] stop throttling indexing: 
numMergesInFlight=4, maxNumMerges=5
MERGING SUCKS

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Scaling Elasticsearch: Merging
• Step 1: Increase shard count from 1 to 5
• Step 2: Disable merge throttling, on ES < 2.0: 
index.store.throttle.type: none
Much better!

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Scaling Elasticsearch: Split Hosts
• Oops, we maxed out CPU! Time to add more nodes

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• Running Logstash and Elasticsearch on separate hosts

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• Running Logstash and Elasticsearch on separate hosts: 
50% throughput improvement: 13k/s -> 19k/s

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Scaling Elasticsearch: Hyperthreading
• YES! About 20% of our performance! Leave it on.

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CPU SCALING
GOVERNORS! 
BUT HOW MUCH?

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Scaling Elasticsearch: CPU Governor
• # echo performance | tee /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor
• ~15-30% performance improvement. Remember to apply at boot!

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Storage

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Scaling Elasticsearch: Disk I/O

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• Common advice
- Use SSD
- RAID 0
- Software RAID is sufficient

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• Uncommon advice
- Good SSDs are important 
Cheap SSDs will make you very, very sad
- Don’t use multiple data paths, use RAID 0 instead 
Heavy translog writes to one disk will bottleneck
- If you have heavy merging, but CPU and disk I/O to spare: 
Extreme case: increase index.merge.scheduler.max_thread_count 
(But try not to…)

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• Uncommon advice
- Reduced durability 
index.translog.durability: async 
Translog fsync() every 5s, may be sufficient with replication
- Cluster recovery eats disk I/O 
Be prepared to tune it up and down during recovery, eg: 
indices.recovery.max_bytes_per_sec: 300mb 
cluster.routing.allocation.cluster_concurrent_rebalance: 24 
cluster.routing.allocation.node_concurrent_recoveries: 2
- Any amount of consistent I/O wait indicates a suboptimal state

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Scaling Elasticsearch: Choosing SSDs
• Consumer grade drives
- Slower writes
- Cheap
- Lower endurance, fewer disk writes per day
• Enterprise grade drives
- Fast
- Expensive
- Higher endurance, higher disk writes per day

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Scaling Elasticsearch: Choosing SSDs
• Read intensive
- Lower endurance, 1-3 DWPD
- Lower write speeds, least expensive
• Mixed use
- Moderate endurance, 10 DWPD
- Balanced read/write performance, pricing middle ground
• Write intensive
- High endurance, 25DWPD
- High write speeds, most expensive

92
YOU MENTIONED
AN FSYNC()
TUNABLE?

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• Remember this graph? Let's make it better!

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Scaling Elasticsearch: Reduced Durability
• Benchmark: Reduced durability. 
Old baseline: ~20k-25k. New baseline: Similar, smoother:

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WHY WAS THE
IMPROVEMENT
SMALLER?

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Scaling Elasticsearch: Thanks, Merges
• MERRRRRRGGGGGGGGGGGGGGGIIIIIIIINNNNGGGGGG!!
• $ curl -s 'http://localhost:9200/_nodes/hot_threads?threads=10' | grep % 
73.6% (367.8ms out of 500ms) 'elasticsearch[es][bulk][T#25]' 
66.8% (334.1ms out of 500ms) 'elasticsearch[es][[logstash][1]: Lucene Merge Thread #139]' 
55.6% (277.9ms out of 500ms) 'elasticsearch[es][[logstash][2]: Lucene Merge Thread #161]'

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…AFTER SOME
LAST WORDS ON
DISK I/O

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Scaling Elasticsearch: Multi-tiered Storage
• Put your most accessed indices across more servers, with more
memory, and faster CPUs.
• Spec out “cold” storage
- SSDs still necessary! Don't even think about spinning platters
- Cram bigger SSDs per server
• Set index.codec: best_compression
• Move indices, re-optimize
• elasticsearch-curator makes this easy

100
Merging

101
WHY DOES THE DEFAULT
CONFIGURATION
MERGE SO MUCH?

102
Scaling Elasticsearch: Default Mapping
• $ curl 'http://localhost:9200/_template/logstash?pretty'
• "string_fields" : { 
"mapping" : { 
"index" : "analyzed", 
"omit_norms" : true, 
"type" : "string", 
"fields" : { 
"raw" : { 
"ignore_above" : 256, 
"index" : "not_analyzed", 
"type" : "string" 
} 
} 
}, 
"match_mapping_type" : "string", 
"match" : "*" 
}
Do you see it?

103
Scaling Elasticsearch: Default Mapping
"mapping" : { 
"index" : "analyzed", 
"type" : "string", 
"fields" : { 
"raw" : { 
"ignore_above" : 256, 
} 
} 
}, 
"match" : "*" 
}
Do you see it?

104
Scaling Elasticsearch: Custom Mapping
"mapping" : { 
}, 
"match" : "*" 
}

105
Scaling Elasticsearch: Custom Mapping
• A small help.. Unfortunately the server is maxed out now! 
Expect this to normally have a bigger impact :-)

106
Indexing performance

107
Scaling Elasticsearch: Indexing Performance
• Increasing bulk thread pool queue can help under bursty indexing
- Be aware of the consequences, you're hiding a performance problem
• Increase index buﬀer
• Increase refresh time, from 1s to 5s
• Spread indexing requests to multiple hosts
• Increase output workers until you stop seeing improvements 
We use num_cpu/2 with transport protocol
• Increase flush_size until you stop seeing improvements 
We use 10,000
• Disk I/O performance

108
• Indexing protocols
- HTTP
- Node
- Transport
• Transport still slightly more performant, but HTTP has closed the gap.
• Node is generally not worth it. Longer start up, more resources, more
fragile, more work for the cluster.

109
• Custom mapping template
- Default template creates an additional not_analyzed .raw field for
every field.
- Every field is analyzed, which eats CPU
- Extra field eats more disk
- Dynamic fields and Hungarian notation
• Use a custom template which has dynamic fields enabled, but has them
not_analyzed 
Ditch .raw fields, unless you really need them
• This change dropped Elasticsearch cluster CPU usage from 28% to 15%

110
• Message complexity matters. 
Adding new lines which are 20k, compared to the average of 1.5k tanked
indexing rate for all log lines:

111
• ruby { code => "if event['message'].length > 10240 then
event['message'] = event['message'].slice!(0,10240) end" }

112
• Speeding up Elasticsearch lets Logstash do more work!

113
Index Size

114
Scaling Elasticsearch: Indices
• Tune shards per index
- num_shards = (num_nodes - failed_node_limit) / (number_of_replicas + 1)
- With 50 nodes, allowing 4 to fail at any time, and 1x replication: 
num_shards = (50 - 4) / (1 + 1) = 23
• If your shards are larger than 25Gb, increase shard count accordingly.
• Tune indices.memory.index_buﬀer_size
- index_buﬀer_size = num_active_shards * 500Mb
- “Active shards”: any shard updated in the last 5 minutes

115
Scaling Elasticsearch: Indices
• Tune refresh_interval
- Defaults to 1s - way too frequent!
- Increase to 5s
- Tuning higher may cause more disk thrashing
- Goal: Flushing as much as your disk’s buﬀer than take
• Example: Samsung SM863 SSDs:
- DRAM buﬀer: 1Gb
- Flush speed: 500Mb/sec

Thank you!
Q&A
@avleen
http://github.com/etsy/logstash-plugins

118
SEC RET ACT 4
Filesystem Comparison

119
5230 segments
29Gb memory
10.5Tb disk space
124 segments
23Gb memory
10.1Tb disk space
OptimizedUnoptimized
Scaling Elasticsearch: Optimize Indices

120
ruby { 
code =>
"event['message'] = event['message'].slice!(0,10240)"
}
ruby {
code =>
"if event['message'].length > 10240; then
event['message'] = event['message'].slice!(0,10240)
end"
}
The Thoughtful WayThe Easy Way

ELK: Moose-ively scaling your log system

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ELK: Moose-ively scaling your log system